1
|
Rykov YG, Ng KP, Patterson MD, Gangwar BA, Kandiah N. Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning. Comput Biol Med 2024; 180:108959. [PMID: 39089109 DOI: 10.1016/j.compbiomed.2024.108959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 07/04/2024] [Accepted: 07/26/2024] [Indexed: 08/03/2024]
Abstract
Neuropsychiatric symptoms (NPS) and mood disorders are common in individuals with mild cognitive impairment (MCI) and increase the risk of progression to dementia. Wearable devices collecting physiological and behavioral data can help in remote, passive, and continuous monitoring of moods and NPS, overcoming limitations and inconveniences of current assessment methods. In this longitudinal study, we examined the predictive ability of digital biomarkers based on sensor data from a wrist-worn wearable to determine the severity of NPS and mood disorders on a daily basis in older adults with predominant MCI. In addition to conventional physiological biomarkers, such as heart rate variability and skin conductance levels, we leveraged deep-learning features derived from physiological data using a self-supervised convolutional autoencoder. Models combining common digital biomarkers and deep features predicted depression severity scores with a correlation of r = 0.73 on average, total severity of mood disorder symptoms with r = 0.67, and mild behavioral impairment scores with r = 0.69 in the study population. Our findings demonstrated the potential of physiological biomarkers collected from wearables and deep learning methods to be used for the continuous and unobtrusive assessments of mental health symptoms in older adults, including those with MCI. TRIAL REGISTRATION: This trial was registered with ClinicalTrials.gov (NCT05059353) on September 28, 2021, titled "Effectiveness and Safety of a Digitally Based Multidomain Intervention for Mild Cognitive Impairment".
Collapse
Affiliation(s)
- Yuri G Rykov
- Neuroglee Therapeutics, 2 Venture Dr, #08-18, Singapore, 608526
| | - Kok Pin Ng
- Department of Neurology, National Neuroscience Institute, 11 Jln Tan Tock Seng, 308433, Singapore; Duke-NUS Medical School, 8 College Rd, 169857, Singapore
| | | | - Bikram A Gangwar
- Neuroglee Therapeutics, 2 Venture Dr, #08-18, Singapore, 608526.
| | - Nagaendran Kandiah
- Dementia Research Centre, Lee Kong Chian School of Medicine, Nanyang Technological University, 11 Mandalay Road, Level 18 308232, Singapore
| |
Collapse
|
2
|
Książek K, Masarczyk W, Głomb P, Romaszewski M, Stokłosa I, Ścisło P, Dębski P, Pudlo R, Buza K, Gorczyca P, Piegza M. Assessment of symptom severity in psychotic disorder patients based on heart rate variability and accelerometer mobility data. Comput Biol Med 2024; 176:108544. [PMID: 38723395 DOI: 10.1016/j.compbiomed.2024.108544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/22/2024] [Accepted: 04/28/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND Advancement in mental health care requires easily accessible, efficient diagnostic and treatment assessment tools. Viable biomarkers could enable objectification and automation of the diagnostic and treatment process, currently dependent on a psychiatric interview. Available wearable technology and computational methods make it possible to incorporate heart rate variability (HRV), an indicator of autonomic nervous system (ANS) activity, into potential diagnostic and treatment assessment frameworks as a biomarker of disease severity in mental disorders, including schizophrenia and bipolar disorder (BD). METHOD We used a commercially available electrocardiography (ECG) chest strap with a built-in accelerometer, i.e. Polar H10, to record R-R intervals and physical activity of 30 hospitalized schizophrenia or BD patients and 30 control participants through ca. 1.5-2 h time periods. We validated a novel approach to data acquisition based on a flexible, patient-friendly and cost-effective setting. We analyzed the relationship between HRV and the Positive and Negative Syndrome Scale (PANSS) test scores, as well as the HRV and mobility coefficient. We also proposed a method of rest period selection based on R-R intervals and mobility data. The source code for reproducing all experiments is available on GitHub, while the dataset is published on Zenodo. RESULTS Mean HRV values were lower in the patient compared to the control group and negatively correlated with the results of the PANSS general subcategory. For the control group, we also discovered the inversely proportional dependency between the mobility coefficient, based on accelerometer data, and HRV. This relationship was less pronounced for the treatment group. CONCLUSIONS HRV value itself, as well as the relationship between HRV and mobility, may be promising biomarkers in disease diagnostics. These findings can be used to develop a flexible monitoring system for symptom severity assessment.
Collapse
Affiliation(s)
- Kamil Książek
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, Gliwice, 44-100, Poland.
| | - Wilhelm Masarczyk
- Department of Psychiatry, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Pyskowicka 49, Tarnowskie Góry, 42-612, Poland
| | - Przemysław Głomb
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, Gliwice, 44-100, Poland
| | - Michał Romaszewski
- Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, Gliwice, 44-100, Poland
| | - Iga Stokłosa
- Department of Psychiatry, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Pyskowicka 49, Tarnowskie Góry, 42-612, Poland
| | - Piotr Ścisło
- Psychiatric Department of the Multidisciplinary Hospital, Tarnowskie Góry, 42-612, Poland
| | - Paweł Dębski
- Institute of Psychology, Humanitas University in Sosnowiec, Kilińskiego 43, Sosnowiec, 41-200, Poland
| | - Robert Pudlo
- Department of Psychoprophylaxis, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Pyskowicka 49, Tarnowskie Góry, 42-612, Poland
| | - Krisztián Buza
- Budapest Business University, Buzogány utca 10-12, Budapest, 1149, Hungary; BioIntelligence Group, Department of Mathematics-Informatics, Sapientia Hungarian University of Transylvania, Târgu Mureş, Romania
| | - Piotr Gorczyca
- Department of Psychiatry, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Pyskowicka 49, Tarnowskie Góry, 42-612, Poland
| | - Magdalena Piegza
- Department of Psychiatry, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Pyskowicka 49, Tarnowskie Góry, 42-612, Poland
| |
Collapse
|
3
|
Ramesh A, Nayak T, Beestrum M, Quer G, Pandit JA. Heart Rate Variability in Psychiatric Disorders: A Systematic Review. Neuropsychiatr Dis Treat 2023; 19:2217-2239. [PMID: 37881808 PMCID: PMC10596135 DOI: 10.2147/ndt.s429592] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/11/2023] [Indexed: 10/27/2023] Open
Abstract
Introduction Heart rate variability (HRV) is a measure of the fluctuation in time interval between consecutive heart beats. Decreased heart rate variability has been shown to have associations with autonomic dysfunction in psychiatric conditions such as depression, substance abuse, anxiety, and schizophrenia, although its use as a prognostic tool remains highly debated. This study aims to review the current literature on heart rate variability as a diagnostic and prognostic tool in psychiatric populations. Methods A literature search was conducted using the MEDLINE, EMBASE, Cochrane, and PsycINFO libraries to identify full-text studies involving adult psychiatric populations that reported HRV measurements. From 1647 originally identified, 31 studies were narrowed down through an abstract and full-text screen. Studies were excluded if they enrolled adolescents or children, used animal models, enrolled patients with another primary diagnosis other than psychiatric as outlined by the diagnostic and statistical manual of mental disorders (DSM) V, or if they assessed HRV in the context of treatment rather than diagnosis. Study quality assessment was conducted using a modified Downs and Blacks quality assessment tool for observational rather than interventional studies. Data were reported in four tables: 1) summarizing study characteristics, 2) methods of HRV detection, 3) key findings and statistics, and 4) quality assessment. Results There is significant variability between studies in their methodology of recording as well as reporting HRV, which makes it difficult to meaningfully interpret data that is clinically applicable due to the presence of significant bias in existing studies. The presence of an association between HRV and the severity of various psychiatric disorders, however, remains promising. Conclusion Future studies should be done to further explore how HRV parameters may be used to enhance the diagnosis and prognosis of several psychiatric disorders.
Collapse
Affiliation(s)
- Ashvita Ramesh
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Tanvi Nayak
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Molly Beestrum
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Giorgio Quer
- Scripps Research Translational Institute, La Jolla, CA, USA
| | - Jay A Pandit
- Scripps Research Translational Institute, La Jolla, CA, USA
| |
Collapse
|
4
|
Oudin A, Maatoug R, Bourla A, Ferreri F, Bonnot O, Millet B, Schoeller F, Mouchabac S, Adrien V. Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health. J Med Internet Res 2023; 25:e44502. [PMID: 37792430 PMCID: PMC10585447 DOI: 10.2196/44502] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 07/10/2023] [Accepted: 08/21/2023] [Indexed: 10/05/2023] Open
Abstract
The term "digital phenotype" refers to the digital footprint left by patient-environment interactions. It has potential for both research and clinical applications but challenges our conception of health care by opposing 2 distinct approaches to medicine: one centered on illness with the aim of classifying and curing disease, and the other centered on patients, their personal distress, and their lived experiences. In the context of mental health and psychiatry, the potential benefits of digital phenotyping include creating new avenues for treatment and enabling patients to take control of their own well-being. However, this comes at the cost of sacrificing the fundamental human element of psychotherapy, which is crucial to addressing patients' distress. In this viewpoint paper, we discuss the advances rendered possible by digital phenotyping and highlight the risk that this technology may pose by partially excluding health care professionals from the diagnosis and therapeutic process, thereby foregoing an essential dimension of care. We conclude by setting out concrete recommendations on how to improve current digital phenotyping technology so that it can be harnessed to redefine mental health by empowering patients without alienating them.
Collapse
Affiliation(s)
- Antoine Oudin
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Redwan Maatoug
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Alexis Bourla
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
- Medical Strategy and Innovation Department, Clariane, Paris, France
- NeuroStim Psychiatry Practice, Paris, France
| | - Florian Ferreri
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Olivier Bonnot
- Department of Child and Adolescent Psychiatry, Nantes University Hospital, Nantes, France
- Pays de la Loire Psychology Laboratory, Nantes University, Nantes, France
| | - Bruno Millet
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Pitié-Salpêtrière Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Félix Schoeller
- Institute for Advanced Consciousness Studies, Santa Monica, CA, United States
- Media Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Stéphane Mouchabac
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
| | - Vladimir Adrien
- Infrastructure for Clinical Research in Neurosciences, Paris Brain Institute, Sorbonne University- Institut national de la santé et de la recherche médicale - Centre national de la recherche scientifique, Paris, France
- Department of Psychiatry, Saint-Antoine Hospital, Public Hospitals of Sorbonne University, Paris, France
| |
Collapse
|
5
|
Heaney J, Buick J, Hadi MU, Soin N. Internet of Things-Based ECG and Vitals Healthcare Monitoring System. MICROMACHINES 2022; 13:2153. [PMID: 36557452 PMCID: PMC9780965 DOI: 10.3390/mi13122153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
Health monitoring and its associated technologies have gained enormous importance over the past few years. The electrocardiogram (ECG) has long been a popular tool for assessing and diagnosing cardiovascular diseases (CVDs). Since the literature on ECG monitoring devices is growing at an exponential rate, it is becoming difficult for researchers and healthcare professionals to select, compare, and assess the systems that meet their demands while also meeting the monitoring standards. This emphasizes the necessity for a reliable reference to guide the design, categorization, and analysis of ECG monitoring systems, which will benefit both academics and practitioners. We present a complete ECG monitoring system in this work, describing the design stages and implementation of an end-to-end solution for capturing and displaying the patient's heart signals, heart rate, blood oxygen levels, and body temperature. The data will be presented on an OLED display, a developed Android application as well as in MATLAB via serial communication. The Internet of Things (IoT) approaches have a clear advantage in tackling the problem of heart disease patient care as they can transform the service mode into a widespread one and alert the healthcare services based on the patient's physical condition. Keeping this in mind, there is also the addition of a web server for monitoring the patient's status via WiFi. The prototype, which is compliant with the electrical safety regulations and medical equipment design, was further benchmarked against a commercially available off-the-shelf device, and showed an excellent accuracy of 99.56%.
Collapse
|
6
|
Understanding the Mechanisms of Sudden Cardiac Death in Bipolar Disorder: Functional Asymmetry in Brain-Heart Interactions as a Potential Culprit. Med Hypotheses 2022. [DOI: 10.1016/j.mehy.2022.110986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
7
|
Cognitive Computing in Mental Healthcare: a Review of Methods and Technologies for Detection of Mental Disorders. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10042-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
|
8
|
Visual electrophysiology and neuropsychology in bipolar disorders: a review on current state and perspectives. Neurosci Biobehav Rev 2022; 140:104764. [DOI: 10.1016/j.neubiorev.2022.104764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/21/2022] [Accepted: 07/01/2022] [Indexed: 11/21/2022]
|
9
|
Mental Health Prediction Using Machine Learning: Taxonomy, Applications, and Challenges. APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2022. [DOI: 10.1155/2022/9970363] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The increase of mental health problems and the need for effective medical health care have led to an investigation of machine learning that can be applied in mental health problems. This paper presents a recent systematic review of machine learning approaches in predicting mental health problems. Furthermore, we will discuss the challenges, limitations, and future directions for the application of machine learning in the mental health field. We collect research articles and studies that are related to the machine learning approaches in predicting mental health problems by searching reliable databases. Moreover, we adhere to the PRISMA methodology in conducting this systematic review. We include a total of 30 research articles in this review after the screening and identification processes. Then, we categorize the collected research articles based on the mental health problems such as schizophrenia, bipolar disorder, anxiety and depression, posttraumatic stress disorder, and mental health problems among children. Discussing the findings, we reflect on the challenges and limitations faced by the researchers on machine learning in mental health problems. Additionally, we provide concrete recommendations on the potential future research and development of applying machine learning in the mental health field.
Collapse
|
10
|
Saccaro LF, Amatori G, Cappelli A, Mazziotti R, Dell'Osso L, Rutigliano G. Portable technologies for digital phenotyping of bipolar disorder: A systematic review. J Affect Disord 2021; 295:323-338. [PMID: 34488086 DOI: 10.1016/j.jad.2021.08.052] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 07/30/2021] [Accepted: 08/22/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Bias-prone psychiatric interviews remain the mainstay of bipolar disorder (BD) assessment. The development of digital phenotyping promises to improve BD management. We present a systematic review of the evidence about the use of portable digital devices for the identification of BD, BD types and BD mood states and for symptom assessment. METHODS We searched Web of KnowledgeSM, Scopus ®, IEEE Xplore, and ACM Digital Library databases (until 5/1/2021) for articles evaluating the use of portable/wearable digital devices, such as smartphone apps, wearable sensors, audio and/or visual recordings, and multimodal tools. The protocol is registered in PROSPERO (CRD42020200086). RESULTS We included 62 studies (2325 BD; 724 healthy controls, HC): 27 using smartphone apps, either for recording self-assessments (n = 10) or for passively gathering metadata (n = 7) or both (n = 10); 15 using wearable sensors for physiological parameters; 17 analysing audio and/or video recordings; 3 using multiple technologies. Two thirds of the included studies applied artificial intelligence (AI)-based approaches. They achieved fair to excellent classification performances. LIMITATIONS The included studies had small sample sizes and marked heterogeneity. Evidence of overfitting emerged, limiting generalizability. The absence of clear guidelines about reporting classification performances, with no shared standard metrics, makes results hardly interpretable and comparable. CONCLUSIONS New technologies offer a noteworthy opportunity to BD digital phenotyping with objectivity and high granularity. AI-based models could deliver important support in clinical decision-making. Further research and cooperation between different stakeholders are needed for addressing methodological, ethical and socio-economic considerations.
Collapse
Affiliation(s)
- Luigi F Saccaro
- Institute of Life Sciences, Sant'Anna School of Advanced Studies, Pisa, Italy; Department of Clinical Neurosciences, Geneva University Hospital (HUG), Geneva, Switzerland
| | - Giulia Amatori
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Andrea Cappelli
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Raffaele Mazziotti
- Institute of Neuroscience of the Italian National Research Council (CNR), Pisa, Italy
| | - Liliana Dell'Osso
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | | |
Collapse
|
11
|
Candia-Rivera D, Catrambone V, Valenza G. The role of electroencephalography electrical reference in the assessment of functional brain-heart interplay: From methodology to user guidelines. J Neurosci Methods 2021; 360:109269. [PMID: 34171310 DOI: 10.1016/j.jneumeth.2021.109269] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 06/16/2021] [Accepted: 06/18/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND The choice of EEG reference has been widely studied. However, the choice of the most appropriate re-referencing for EEG data is still debated. Moreover, the role of EEG reference in the estimation of functional Brain-Heart Interplay (BHI), together with different multivariate modelling strategies, has not been investigated yet. METHODS This study identifies the best methodology combining a proper EEG electrical reference and signal processing methods for an effective functional BHI assessment. The effects of the EEG reference among common average, mastoids average, Laplacian reference, Cz reference, and the reference electrode standardization technique (REST) were explored throughout different BHI methods including synthetic data generation (SDG) model, heartbeat-evoked potentials, heartbeat-evoked oscillations, and maximal information coefficient. RESULTS The SDG model exhibited high robustness between EEG references, whereas the maximal information coefficient method exhibited a high sensitivity. The common average and REST references for EEG showed a good consistency in the between-method comparisons. Laplacian, and Cz references significantly bias a BHI measurement. COMPARISON WITH EXISTING METHODS The use of EEG reference based on a common average outperforms on the use of other references for consistency in estimating directed functional BHI. We do not recommend the use of EEG references based on analytical derivations as the experimental conditions may not meet the requirements of their optimal estimation, particularly in clinical settings. CONCLUSION The use of a common average for EEG electrical reference is concluded to be the most appropriate choice for a quantitative, functional BHI assessment.
Collapse
Affiliation(s)
- Diego Candia-Rivera
- Bioengineering and Robotics Research Center E. Piaggio and the Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy.
| | - Vincenzo Catrambone
- Bioengineering and Robotics Research Center E. Piaggio and the Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy
| | - Gaetano Valenza
- Bioengineering and Robotics Research Center E. Piaggio and the Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy
| |
Collapse
|
12
|
Gregório ML, Wazen GLL, Kemp AH, Milan-Mattos JC, Porta A, Catai AM, de Godoy MF. Non-linear analysis of the heart rate variability in characterization of manic and euthymic phases of bipolar disorder. J Affect Disord 2020; 275:136-144. [PMID: 32658816 DOI: 10.1016/j.jad.2020.07.012] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 06/18/2020] [Accepted: 07/05/2020] [Indexed: 01/25/2023]
Abstract
BACKGROUND - Bipolar Disorder (BD) has been associated with autonomic nervous system (ANS) dysregulation, with a consequent increase in mortality. Recent work highlights the non-linear analysis of ANS function. Our objective was to compare ANS modulation using recurrence plots (RP) and symbolic analysis (SA) in manic and euthymic phases of BD to controls. METHODS - Eighteen male patients (33.1 ± 12.0 years) were assessed during mania and at discharge in the euthymic phase compared and to a healthy group matched by age (33.9 ± 10.8 years). Electrocardiographic series (1000 RR intervals, at rest, in supine position) were captured using Polar Advantage RS800CX equipment and Heart Rate Variability (HRV) was analysed using RP and SA. Statistical analysis was performed using ANOVA with Tukey's post-test. The threshold for statistical significance was set at P < 0.05 and Cohen's d effect size was also quantified considering d > 0.8 as an important effect. The study was registered into the Clinical Trials Registration (ClinicalTrials.gov: NCT01272518). RESULTS Manic group presented significantly higher linearity before treatment (P<0.05) compared to controls considering RP variables. Cohen's d values had a large effect size ranging from 0.888 to 1.227. In the manic phase, SA showed predominance of the sympathetic component (OV%) with reduction of the parasympathetic component (2LV% and 2UV%) with reversion post treatment including higher Shannon Entropy (SE) indicating higher complexity. LIMITATIONS - short follow-up (1 month) and small number of patients. CONCLUSIONS - Non-linear analyzes may be used as supplementary tools for understanding autonomic function in BD during mania and after drug treatment.
Collapse
Affiliation(s)
- Michele Lima Gregório
- Transdisciplinary Nucleus for the Study of Chaos and Complexity, NUTECC, São José do Rio Preto Medical School, FAMERP, Avenida Brigadeiro Faria Lima, 54-16 CEP, 15090-000 São José do Rio Preto, SP, Brazil.
| | - Guilherme Luiz Lopes Wazen
- Department of Psychiatry, São José do Rio Preto Medical School, FAMERP, Avenida Brigadeiro Faria Lima, 54-16 CEP, 15090-000 São José do Rio Preto, SP, Brazil
| | - Andrew Haddon Kemp
- Department of Psychology, College of Human and Health Sciences, Swansea University, Singleton Park, Wales SA2 8PP, United Kingdom
| | - Juliana Cristina Milan-Mattos
- Cardiovascular Physical Therapy Laboratory, Department of Physical Therapy, Federal University of São Carlos, São Paulo, Brazil
| | - Alberto Porta
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy; Department of Cardiothoracic, Vascular Anesthesia and Intensive Care, IRCCS Policlinico San Donato, Milan, Italy.
| | - Aparecida Maria Catai
- Cardiovascular Physical Therapy Laboratory, Department of Physical Therapy, Federal University of São Carlos, São Paulo, Brazil.
| | - Moacir Fernandes de Godoy
- Transdisciplinary Nucleus for the Study of Chaos and Complexity, NUTECC, São José do Rio Preto Medical School, FAMERP, Avenida Brigadeiro Faria Lima, 54-16 CEP, 15090-000 São José do Rio Preto, SP, Brazil; Department of Cardiology and Cardiovascular Surgery, São José do Rio Preto Medical School, FAMERP, Avenida Brigadeiro Faria Lima, 5416 CEP, 15090-000 São José do Rio Preto, SP, Brazil
| |
Collapse
|
13
|
Khundaqji H, Hing W, Furness J, Climstein M. Smart Shirts for Monitoring Physiological Parameters: Scoping Review. JMIR Mhealth Uhealth 2020; 8:e18092. [PMID: 32348279 PMCID: PMC7287746 DOI: 10.2196/18092] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 03/10/2020] [Accepted: 03/22/2020] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND The recent trends of technological innovation and widescale digitization as potential solutions to challenges in health care, sports, and emergency service operations have led to the conception of smart textile technology. In health care, these smart textile systems present the potential to aid preventative medicine and early diagnosis through continuous, noninvasive tracking of physical and mental health while promoting proactive involvement of patients in their medical management. In areas such as sports and emergency response, the potential to provide comprehensive and simultaneous physiological insights across multiple body systems is promising. However, it is currently unclear what type of evidence exists surrounding the use of smart textiles for the monitoring of physiological outcome measures across different settings. OBJECTIVE This scoping review aimed to systematically survey the existing body of scientific literature surrounding smart textiles in their most prevalent form, the smart shirt, for monitoring physiological outcome measures. METHODS A total of 5 electronic bibliographic databases were systematically searched (Ovid Medical Literature Analysis and Retrieval System Online, Excerpta Medica database, Scopus, Cumulative Index to Nursing and Allied Health Literature, and SPORTDiscus). Publications from the inception of the database to June 24, 2019 were reviewed. Nonindexed literature relevant to this review was also systematically searched. The results were then collated, summarized, and reported. RESULTS Following the removal of duplicates, 7871 citations were identified. On the basis of title and abstract screening, 7632 citations were excluded, whereas 239 were retrieved and assessed for eligibility. Of these, 101 citations were included in the final analysis. Included studies were categorized into four themes: (1) prototype design, (2) validation, (3) observational, and (4) reviews. Among the 101 analyzed studies, prototype design was the most prevalent theme (50/101, 49.5%), followed by validation (29/101, 28.7%), observational studies (21/101, 20.8%), and reviews (1/101, 0.1%). Presented prototype designs ranged from those capable of monitoring one physiological metric to those capable of monitoring several simultaneously. In 29 validation studies, 16 distinct smart shirts were validated against reference technology under various conditions and work rates, including rest, submaximal exercise, and maximal exercise. The identified observational studies used smart shirts in clinical, healthy, and occupational populations for aims such as early diagnosis and stress detection. One scoping review was identified, investigating the use of smart shirts for electrocardiograph signal monitoring in cardiac patients. CONCLUSIONS Although smart shirts have been found to be valid and reliable in the monitoring of specific physiological metrics, results were variable for others, demonstrating the need for further systematic validation. Analysis of the results has also demonstrated gaps in knowledge, such as a considerable lag of validation and observational studies in comparison with prototype design and limited investigation using smart shirts in pediatric, elite sports, and emergency service populations.
Collapse
Affiliation(s)
- Hamzeh Khundaqji
- Faculty of Health Sciences & Medicine, Bond University, Gold Coast, Australia
| | - Wayne Hing
- Faculty of Health Sciences & Medicine, Bond University, Gold Coast, Australia
| | - James Furness
- Faculty of Health Sciences & Medicine, Bond University, Gold Coast, Australia
| | - Mike Climstein
- School of Health and Human Sciences, Southern Cross University, Bilinga, Australia.,Physical Activity, Lifestyle, Ageing and Wellbeing Faculty Research Group, University of Sydney, Sydney, Australia
| |
Collapse
|
14
|
Serhani MA, T. El Kassabi H, Ismail H, Nujum Navaz A. ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1796. [PMID: 32213969 PMCID: PMC7147367 DOI: 10.3390/s20061796] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 03/17/2020] [Accepted: 03/19/2020] [Indexed: 02/01/2023]
Abstract
Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems' components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems' value chain is conducted, and a thorough review of the relevant literature, classified against the experts' taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.
Collapse
Affiliation(s)
- Mohamed Adel Serhani
- Department of Information Systems and Security, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates;
| | - Hadeel T. El Kassabi
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates; (H.T.E.K.)
| | - Heba Ismail
- Department of Computer Science and Software Engineering, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates; (H.T.E.K.)
| | - Alramzana Nujum Navaz
- Department of Information Systems and Security, College of Information Technology, UAE University, Al Ain 15551, United Arab Emirates;
| |
Collapse
|
15
|
Nardelli M, Valenza G, Greco A, Lanatá A, Scilingo EP, Bailón R. Quantifying the lagged Poincaré plot geometry of ultrashort heart rate variability series: automatic recognition of odor hedonic tone. Med Biol Eng Comput 2020; 58:1099-1112. [PMID: 32162243 DOI: 10.1007/s11517-019-02095-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 12/06/2019] [Indexed: 10/24/2022]
Abstract
The application of Poincaré plot analysis to characterize inter-beat interval dynamics has been successfully proposed in the scientific literature for the assessment of humans' physiological states and related aberrations. In this study, we proposed novel descriptors to trace the evolution of Poincaré plot shape over the lags. Their reliability in ultra-short cardiovascular series analysis was validated on synthetic inter-beat series generated through a physiologically plausible integral pulse frequency modulation model. Furthermore, we used the proposed approach for the investigation of the direct relationship between autonomic nervous system (ANS) dynamics and hedonic olfactory elicitation, in a group of 30 healthy subjects. Participants with a similar olfactory threshold were selected, and were asked to score 5-s stimuli in terms of arousal and valence levels according to the Russell's circumflex model of affect. Their ANS response was investigated in 35-s windows after the elicitation. Experimental results showed a gender-specific, high discriminant power of the proposed approach, discerning between pleasant and unpleasant odorants with an accuracy of 83.33% and 73.33% for men and for women, respectively. Graphical Abstract Olfaction plays a crucial role in our life and is strictly related to the Autonomic Nervous System (ANS) activity, which can be monitored studying Heart Rate Variability. We used the Lagged Poincare Plot approach to recognize gender-specific ANS response in 35-second windows after the elicitation through pleasant/unpleasant odorants.
Collapse
Affiliation(s)
- M Nardelli
- Department of Information Engineering, Research Centre E. Piaggio, University of Pisa, Via G. Caruso 16, Pisa, Italy.
| | - G Valenza
- Department of Information Engineering, Research Centre E. Piaggio, University of Pisa, Via G. Caruso 16, Pisa, Italy
| | - A Greco
- Department of Information Engineering, Research Centre E. Piaggio, University of Pisa, Via G. Caruso 16, Pisa, Italy
| | - A Lanatá
- Department of Information Engineering, Research Centre E. Piaggio, University of Pisa, Via G. Caruso 16, Pisa, Italy
| | - E P Scilingo
- Department of Information Engineering, Research Centre E. Piaggio, University of Pisa, Via G. Caruso 16, Pisa, Italy
| | - R Bailón
- BSICoS Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain.,CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER - BBN), Madrid, Spain
| |
Collapse
|
16
|
Statistical Analysis on Heart Rate Variability for Graded Cardiopulmonary Groups with Different Exercise Intensities. J Med Biol Eng 2020. [DOI: 10.1007/s40846-020-00514-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
17
|
Byun S, Kim AY, Jang EH, Kim S, Choi KW, Yu HY, Jeon HJ. Entropy analysis of heart rate variability and its application to recognize major depressive disorder: A pilot study. Technol Health Care 2020; 27:407-424. [PMID: 31045557 PMCID: PMC6597986 DOI: 10.3233/thc-199037] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND The current method to evaluate major depressive disorder (MDD) relies on subjective clinical interviews and self-questionnaires. OBJECTIVE Autonomic imbalance in MDD patients is characterized using entropy measures of heart rate variability (HRV). A machine learning approach for screening depression based on the entropy is demonstrated. METHODS The participants experience five experimental phases: baseline (BASE), stress task (MAT), stress task recovery (REC1), relaxation task (RLX), and relaxation task recovery (REC2). The four entropy indices, approximate entropy, sample entropy, fuzzy entropy, and Shannon entropy, are extracted for each phase, and a total of 20 features are used. A support vector machine classifier and recursive feature elimination are employed for classification. RESULTS The entropy features are lower in the MDD group; however, the disease does not have a significant effect. Experimental tasks significantly affect the features. The entropy did not recover during REC1. The differences in the entropy features between the two groups increased after MAT and showed the largest gap in REC2. We achieved 70% accuracy, 64% sensitivity, and 76% specificity with three optimal features during RLX and REC2. CONCLUSION Monitoring of HRV complexity changes when a subject experiences autonomic arousal and recovery can potentially facilitate objective depression recognition.
Collapse
Affiliation(s)
- Sangwon Byun
- Department of Electronics Engineering, Incheon National University, Incheon 22012, Korea
| | - Ah Young Kim
- Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
| | - Eun Hye Jang
- Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
| | - Seunghwan Kim
- Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
| | - Kwan Woo Choi
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.,Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Korea
| | - Han Young Yu
- Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
| | - Hong Jin Jeon
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
| |
Collapse
|
18
|
Hagihara H, Horikawa T, Irino Y, Nakamura HK, Umemori J, Shoji H, Yoshida M, Kamitani Y, Miyakawa T. Peripheral blood metabolome predicts mood change-related activity in mouse model of bipolar disorder. Mol Brain 2019; 12:107. [PMID: 31822292 PMCID: PMC6902552 DOI: 10.1186/s13041-019-0527-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Accepted: 11/26/2019] [Indexed: 12/27/2022] Open
Abstract
Bipolar disorder is a major mental illness characterized by severe swings in mood and activity levels which occur with variable amplitude and frequency. Attempts have been made to identify mood states and biological features associated with mood changes to compensate for current clinical diagnosis, which is mainly based on patients' subjective reports. Here, we used infradian (a cycle > 24 h) cyclic locomotor activity in a mouse model useful for the study of bipolar disorder as a proxy for mood changes. We show that metabolome patterns in peripheral blood could retrospectively predict the locomotor activity levels. We longitudinally monitored locomotor activity in the home cage, and subsequently collected peripheral blood and performed metabolomic analyses. We then constructed cross-validated linear regression models based on blood metabolome patterns to predict locomotor activity levels of individual mice. Our analysis revealed a significant correlation between actual and predicted activity levels, indicative of successful predictions. Pathway analysis of metabolites used for successful predictions showed enrichment in mitochondria metabolism-related terms, such as "Warburg effect" and "citric acid cycle." In addition, we found that peripheral blood metabolome patterns predicted expression levels of genes implicated in bipolar disorder in the hippocampus, a brain region responsible for mood regulation, suggesting that the brain-periphery axis is related to mood-change-associated behaviors. Our results may serve as a basis for predicting individual mood states through blood metabolomics in bipolar disorder and other mood disorders and may provide potential insight into systemic metabolic activity in relation to mood changes.
Collapse
Affiliation(s)
- Hideo Hagihara
- Division of Systems Medical Science, Institute for Comprehensive Medical Science, Fujita Health University, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Tomoyasu Horikawa
- Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Kyoto, 619-0288, Japan
| | - Yasuhiro Irino
- Division of Evidence-based Laboratory Medicine, Kobe University, Graduate School of Medicine, Kobe, 650-0017, Japan
| | - Hironori K Nakamura
- Division of Systems Medical Science, Institute for Comprehensive Medical Science, Fujita Health University, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Juzoh Umemori
- Division of Systems Medical Science, Institute for Comprehensive Medical Science, Fujita Health University, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Hirotaka Shoji
- Division of Systems Medical Science, Institute for Comprehensive Medical Science, Fujita Health University, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Masaru Yoshida
- Division of Metabolomics Research, Department of Internal Related, Kobe University Graduate School of Medicine, Kobe, 650-0017, Japan
- Division of Gastroenterology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, 650-0017, Japan
| | - Yukiyasu Kamitani
- Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Kyoto, 619-0288, Japan
- Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan
| | - Tsuyoshi Miyakawa
- Division of Systems Medical Science, Institute for Comprehensive Medical Science, Fujita Health University, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
| |
Collapse
|
19
|
Jungmann SM, Klan T, Kuhn S, Jungmann F. Accuracy of a Chatbot (Ada) in the Diagnosis of Mental Disorders: Comparative Case Study With Lay and Expert Users. JMIR Form Res 2019; 3:e13863. [PMID: 31663858 PMCID: PMC6914276 DOI: 10.2196/13863] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 05/26/2019] [Accepted: 08/31/2019] [Indexed: 12/28/2022] Open
Abstract
Background Health apps for the screening and diagnosis of mental disorders have emerged in recent years on various levels (eg, patients, practitioners, and public health system). However, the diagnostic quality of these apps has not been (sufficiently) tested so far. Objective The objective of this pilot study was to investigate the diagnostic quality of a health app for a broad spectrum of mental disorders and its dependency on expert knowledge. Methods Two psychotherapists, two psychology students, and two laypersons each read 20 case vignettes with a broad spectrum of mental disorders. They used a health app (Ada—Your Health Guide) to get a diagnosis by entering the symptoms. Interrater reliabilities were computed between the diagnoses of the case vignettes and the results of the app for each user group. Results Overall, there was a moderate diagnostic agreement (kappa=0.64) between the results of the app and the case vignettes for mental disorders in adulthood and a low diagnostic agreement (kappa=0.40) for mental disorders in childhood and adolescence. When psychotherapists applied the app, there was a good diagnostic agreement (kappa=0.78) regarding mental disorders in adulthood. The diagnostic agreement was moderate (kappa=0.55/0.60) for students and laypersons. For mental disorders in childhood and adolescence, a moderate diagnostic quality was found when psychotherapists (kappa=0.53) and students (kappa=0.41) used the app, whereas the quality was low for laypersons (kappa=0.29). On average, the app required 34 questions to be answered and 7 min to complete. Conclusions The health app investigated here can represent an efficient diagnostic screening or help function for mental disorders in adulthood and has the potential to support especially diagnosticians in their work in various ways. The results of this pilot study provide a first indication that the diagnostic accuracy is user dependent and improvements in the app are needed especially for mental disorders in childhood and adolescence.
Collapse
Affiliation(s)
| | - Timo Klan
- Department of Psychology, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Sebastian Kuhn
- University Medical Center, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Florian Jungmann
- University Medical Center, Johannes Gutenberg-University Mainz, Mainz, Germany
| |
Collapse
|
20
|
Byun S, Kim AY, Jang EH, Kim S, Choi KW, Yu HY, Jeon HJ. Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol. Comput Biol Med 2019; 112:103381. [DOI: 10.1016/j.compbiomed.2019.103381] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 08/02/2019] [Accepted: 08/03/2019] [Indexed: 01/15/2023]
|
21
|
Redpath N, Rackers HS, Kimmel MC. The Relationship Between Perinatal Mental Health and Stress: a Review of the Microbiome. Curr Psychiatry Rep 2019; 21:18. [PMID: 30826885 DOI: 10.1007/s11920-019-0998-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
PURPOSE OF REVIEW Our current understanding of the underlying mechanisms and etiologies of perinatal mood and anxiety disorders (PMADs) is not clearly identified. The relationship of stress-induced adaptations (i.e., the hypothalamic-pituitary-adrenal (HPA) axis, the autonomic nervous system (ANS), the immune system) and the microbiota are potential contributors to psychopathology exhibited in women during pregnancy and postpartum and should be investigated. RECENT FINDINGS The stress response activates the HPA axis and dysregulates the ANS, leading to the inhibition of the parasympathetic system. Sustained high levels of cortisol, reduced heart variability, and modulated immune responses increase the vulnerability to PMAD. Bidirectional communication between the nervous system and the microbiota is an important factor to alter host homeostasis and development of PMAD. Future research in the relationship between the psychoneuroimmune system, the gut microbiota, and PMAD has the potential to be integrated in clinical practice to improve screening, diagnosis, and treatment.
Collapse
Affiliation(s)
- Nusiebeh Redpath
- Department of Maternal and Child Health, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hannah S Rackers
- Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Mary C Kimmel
- Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| |
Collapse
|
22
|
Kim AY, Jang EH, Kim S, Choi KW, Jeon HJ, Yu HY, Byun S. Automatic detection of major depressive disorder using electrodermal activity. Sci Rep 2018; 8:17030. [PMID: 30451895 PMCID: PMC6242826 DOI: 10.1038/s41598-018-35147-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 10/31/2018] [Indexed: 02/03/2023] Open
Abstract
Major depressive disorder (MDD) is a common psychiatric disorder and the leading cause of disability worldwide. However, current methods used to diagnose depression mainly rely on clinical interviews and self-reported scales of depressive symptoms, which lack objectivity and efficiency. To address this challenge, we present a machine learning approach to screen for MDD using electrodermal activity (EDA). Participants included 30 patients with MDD and 37 healthy controls. Their EDA was measured during five experimental phases consisted of baseline, mental arithmetic task, recovery from the stress task, relaxation task, and recovery from the relaxation task, which elicited multiple alterations in autonomic activity. Selected EDA features were extracted from each phase, and differential EDA features between two distinct phases were evaluated. By using these features as input data and performing feature selection with SVM-RFE, 74% accuracy, 74% sensitivity, and 71% specificity could be achieved by our decision tree classifier. The most relevant features selected by SVM-RFE included differential EDA features and features from the stress and relaxation tasks. These findings suggest that automatic detection of depression based on EDA features is feasible and that monitoring changes in physiological signal when a subject is experiencing autonomic arousal and recovery may enhance discrimination power.
Collapse
Affiliation(s)
- Ah Young Kim
- Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon, Korea
| | - Eun Hye Jang
- Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon, Korea
| | - Seunghwan Kim
- Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon, Korea
| | - Kwan Woo Choi
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Hong Jin Jeon
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Han Young Yu
- Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon, Korea.
| | - Sangwon Byun
- Department of Electronics Engineering, Incheon National University, Incheon, Korea.
| |
Collapse
|
23
|
Marín-Morales J, Higuera-Trujillo JL, Greco A, Guixeres J, Llinares C, Scilingo EP, Alcañiz M, Valenza G. Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors. Sci Rep 2018; 8:13657. [PMID: 30209261 PMCID: PMC6135750 DOI: 10.1038/s41598-018-32063-4] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Accepted: 08/10/2018] [Indexed: 11/30/2022] Open
Abstract
Affective Computing has emerged as an important field of study that aims to develop systems that can automatically recognize emotions. Up to the present, elicitation has been carried out with non-immersive stimuli. This study, on the other hand, aims to develop an emotion recognition system for affective states evoked through Immersive Virtual Environments. Four alternative virtual rooms were designed to elicit four possible arousal-valence combinations, as described in each quadrant of the Circumplex Model of Affects. An experiment involving the recording of the electroencephalography (EEG) and electrocardiography (ECG) of sixty participants was carried out. A set of features was extracted from these signals using various state-of-the-art metrics that quantify brain and cardiovascular linear and nonlinear dynamics, which were input into a Support Vector Machine classifier to predict the subject’s arousal and valence perception. The model’s accuracy was 75.00% along the arousal dimension and 71.21% along the valence dimension. Our findings validate the use of Immersive Virtual Environments to elicit and automatically recognize different emotional states from neural and cardiac dynamics; this development could have novel applications in fields as diverse as Architecture, Health, Education and Videogames.
Collapse
Affiliation(s)
- Javier Marín-Morales
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, València, Spain.
| | - Juan Luis Higuera-Trujillo
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, València, Spain
| | - Alberto Greco
- Bioengineering and Robotics Research Centre E Piaggio & Department of Information Engineering, University of Pisa, Pisa, Italy
| | - Jaime Guixeres
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, València, Spain
| | - Carmen Llinares
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, València, Spain
| | - Enzo Pasquale Scilingo
- Bioengineering and Robotics Research Centre E Piaggio & Department of Information Engineering, University of Pisa, Pisa, Italy
| | - Mariano Alcañiz
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, València, Spain
| | - Gaetano Valenza
- Bioengineering and Robotics Research Centre E Piaggio & Department of Information Engineering, University of Pisa, Pisa, Italy
| |
Collapse
|
24
|
Darzi A, Gaweesh SM, Ahmed MM, Novak D. Identifying the Causes of Drivers' Hazardous States Using Driver Characteristics, Vehicle Kinematics, and Physiological Measurements. Front Neurosci 2018; 12:568. [PMID: 30154696 PMCID: PMC6102354 DOI: 10.3389/fnins.2018.00568] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 07/27/2018] [Indexed: 11/13/2022] Open
Abstract
Drivers’ hazardous physical and mental states (e.g., distraction, fatigue, stress, and high workload) have a major effect on driving performance and strongly contribute to 25–50% of all traffic accidents. They are caused by numerous factors, such as cell phone use or lack of sleep. However, while significant research has been done on detecting hazardous states, most studies have not tried to identify the causes of the hazardous states. Such information would be very useful, as it would allow intelligent vehicles to better respond to a detected hazardous state. Thus, this study examined whether the cause of a driver’s hazardous state can be automatically identified using a combination of driver characteristics, vehicle kinematics, and physiological measures. Twenty-one healthy participants took part in four 45-min sessions of simulated driving, of which they were mildly sleep-deprived for two sessions. Within each session, there were eight different scenarios with different weather (sunny or snowy), traffic density and cell phone usage (with or without cell phone). During each scenario, four physiological (respiration, electrocardiogram, skin conductance, and body temperature) and eight vehicle kinematics measures were monitored. Additionally, three self-reported driver characteristics were obtained: personality, stress level, and mood. Three feature sets were formed based on driver characteristics, vehicle kinematics, and physiological signals. All possible combinations of the three feature sets were used to classify sleep deprivation (drowsy vs. alert), traffic density (low vs. high), cell phone use, and weather conditions (foggy/snowy vs. sunny) with highest accuracies of 98.8%, 91.4%, 82.3%, and 71.5%, respectively. Vehicle kinematics were most useful for classification of weather and traffic density while physiology and driver characteristics were useful for classification of sleep deprivation and cell phone use. Furthermore, a second classification scheme was tested that also incorporates information about whether or not other causes of hazardous states are present, though this did not result in higher classification accuracy. In the future, these classifiers could be used to identify both the presence and cause of a driver’s hazardous state, which could serve as the basis for more intelligent intervention systems.
Collapse
Affiliation(s)
- Ali Darzi
- Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY, United States
| | - Sherif M Gaweesh
- Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY, United States
| | - Mohamed M Ahmed
- Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY, United States
| | - Domen Novak
- Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY, United States
| |
Collapse
|
25
|
Rohani DA, Faurholt-Jepsen M, Kessing LV, Bardram JE. Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review. JMIR Mhealth Uhealth 2018; 6:e165. [PMID: 30104184 PMCID: PMC6111148 DOI: 10.2196/mhealth.9691] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 05/13/2018] [Accepted: 06/18/2018] [Indexed: 12/14/2022] Open
Abstract
Background Several studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders (unipolar and bipolar disorders). However, individual studies have reported on different and sometimes contradicting results, and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published. Objective The objectives of this systematic review were to (1) provide an overview of the correlations between objective behavioral features and depressive mood symptoms reported in the literature and (2) investigate the strength and statistical significance of these correlations across studies. The answers to these questions could potentially help identify which objective features have shown most promising results across studies. Methods We conducted a systematic review of the scientific literature, reported according to the preferred reporting items for systematic reviews and meta-analyses guidelines. IEEE Xplore, ACM Digital Library, Web of Sciences, PsychINFO, PubMed, DBLP computer science bibliography, HTA, DARE, Scopus, and Science Direct were searched and supplemented by hand examination of reference lists. The search ended on April 27, 2017, and was limited to studies published between 2007 and 2017. Results A total of 46 studies were eligible for the review. These studies identified and investigated 85 unique objective behavioral features, covering 17 various sensor data inputs. These features were divided into 7 categories. Several features were found to have statistically significant and consistent correlation directionality with mood assessment (eg, the amount of home stay, sleep duration, and vigorous activity), while others showed directionality discrepancies across the studies (eg, amount of text messages [short message service] sent, time spent between locations, and frequency of mobile phone screen activity). Conclusions Several studies showed consistent and statistically significant correlations between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms. Hence, continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms. However, the evidence is limited by methodological issues in individual studies and by a lack of standardization of (1) the collected objective features, (2) the mood assessment methodology, and (3) the statistical methods applied. Therefore, consistency in data collection and analysis in future studies is needed, making replication studies as well as meta-analyses possible.
Collapse
Affiliation(s)
- Darius A Rohani
- Embedded Systems Engineering, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.,Copenhagen Center for Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Maria Faurholt-Jepsen
- Copenhagen Affective Disorder Research Centre, Psychiatric Centre Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Lars Vedel Kessing
- Copenhagen Affective Disorder Research Centre, Psychiatric Centre Copenhagen, Rigshospitalet, Copenhagen, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jakob E Bardram
- Embedded Systems Engineering, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.,Copenhagen Center for Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| |
Collapse
|
26
|
Nardelli M, Greco A, Bolea J, Valenza G, Scilingo EP, Bailon R. Investigation of Lagged Poincaré Plot reliability in ultra-short synthetic and experimental Heart Rate Variability series. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:2329-2332. [PMID: 29060364 DOI: 10.1109/embc.2017.8037322] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This study reports on the reliability of Lagged Poincaré Plot (LPP) parameters calculated from ultra-short cardiovascular time series (from 30 to 180 seconds). ity (HRV) signals, whereas a few studies have studied nonlinear approaches. Particularly, methods derived from the phase-space theory, especially the ones employing multi-lag analyses, are usually considered to be inaccurate with a low number of samples. Here we propose a comprehensive study about LPP, using both synthetic and real RR series. Specifically, we considered 109 5-minutes HRV series: 60 synthetic series generated through the Integral Pulse Frequency Modulation (IPFM) model and 49 experimental series acquired from healthy subjects during resting-state. Three parameters have been extracted through the ellipse-fitting method, SD1, SD2 and S, using ten values of lag. All LPP parameters were estimated by averaging estimates gathered from segments of 30, 120 and 180 seconds, and compared with the once from 5-minute series. Results showed Spearman's correlation coefficients higher than 0.9 in both synthetic and real series. In conclusion, SD1 gave promising results in terms of percentage absolute error, when it was extracted from series with a duration less than three minutes.
Collapse
|
27
|
Poli R, Valenza G, Scilingo EP, Citi L. Self-reported well-being score modelling and prediction: Proof-of-concept of an approach based on linear dynamic systems. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:2205-2208. [PMID: 29060334 DOI: 10.1109/embc.2017.8037292] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Assessment and recognition of perceived well-being has wide applications in the development of assistive healthcare systems for people with physical and mental disorders. In practical data collection, these systems need to be less intrusive, and respect users' autonomy and willingness as much as possible. As a result, self-reported data are not necessarily available at all times. Conventional classifiers, which usually require feature vectors of a prefixed dimension, are not well suited for this problem. To address the issue of non-uniformly sampled measurements, in this study we propose a method for the modelling and prediction of self-reported well-being scores based on a linear dynamic system. Within the model, we formulate different features as observations, making predictions even in the presence of inconsistent and irregular data. We evaluate the proposed method with synthetic data, as well as real data from two patients diagnosed with cancer. In the latter, self-reported scores from three well-being-related scales were collected over a period of approximately 60 days. Prompted each day, the patients had the choice whether to respond or not. Results show that the proposed model is able to track and predict the patients' perceived well-being dynamics despite the irregularly sampled data.
Collapse
|
28
|
Nardelli M, Lanata A, Bertschy G, Scilingo EP, Valenza G. Heartbeat Complexity Modulation in Bipolar Disorder during Daytime and Nighttime. Sci Rep 2017; 7:17920. [PMID: 29263393 PMCID: PMC5738374 DOI: 10.1038/s41598-017-18036-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Accepted: 11/30/2017] [Indexed: 02/07/2023] Open
Abstract
This study reports on the complexity modulation of heartbeat dynamics in patients affected by bipolar disorder. In particular, a multiscale entropy analysis was applied to the R-R interval series, that were derived from electrocardiographic (ECG) signals for a group of nineteen subjects comprised of eight patients and eleven healthy control subjects. They were monitored using a textile-based sensorized t-shirt during the day and overnight for a total of 47 diurnal and 27 nocturnal recordings. Patients showed three different mood states: depression, hypomania and euthymia. Results show a clear loss of complexity during depressive and hypomanic states as compared to euthymic and healthy control states. In addition, we observed that a more significant complexity modulation among healthy and pathological mood states occurs during the night. These findings suggest that bipolar disorder is associated with an enhanced sleep-related dysregulation of the Autonomic Nervous System (ANS) activity, and that heartbeat complex dynamics may serve as a viable marker of pathological conditions in mental health.
Collapse
Affiliation(s)
- Mimma Nardelli
- Computational Physiology and Biomedical Instruments group, Department of Information Engineering & Bioengineering and Robotics Research Centre E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy.
| | - Antonio Lanata
- Computational Physiology and Biomedical Instruments group, Department of Information Engineering & Bioengineering and Robotics Research Centre E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
| | - Gilles Bertschy
- Department of Psychiatry and Mental Health, Strasbourg University Hospital, INSERM U1114, University of Strasbourg - F-67000, Strasbourg, France
| | - Enzo Pasquale Scilingo
- Computational Physiology and Biomedical Instruments group, Department of Information Engineering & Bioengineering and Robotics Research Centre E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
| | - Gaetano Valenza
- Computational Physiology and Biomedical Instruments group, Department of Information Engineering & Bioengineering and Robotics Research Centre E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
| |
Collapse
|
29
|
Assessing mood symptoms through heartbeat dynamics: An HRV study on cardiosurgical patients. J Psychiatr Res 2017; 95:179-188. [PMID: 28865333 DOI: 10.1016/j.jpsychires.2017.08.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Revised: 08/20/2017] [Accepted: 08/25/2017] [Indexed: 12/18/2022]
Abstract
BACKGROUND Heart Rate Variability (HRV) is reduced both in depression and in coronary heart disease (CHD) suggesting common pathophysiological mechanisms for the two disorders. Within CHD, cardiac surgery patients (CSP) with postoperative depression are at greater risk of adverse cardiac events. Therefore, CSP would especially benefit from depression early diagnosis. Here we tested whether HRV-multi-feature analysis discriminates CSP with or without depression and provides an effective estimation of symptoms severity. METHODS Thirty-one patients admitted to cardiac rehabilitation after first-time cardiac surgery were recruited. Depressive symptoms were assessed with the Center for Epidemiologic Studies Depression Scale (CES-D). HRV features in time, frequency, and nonlinear domains were extracted from 5-min-ECG recordings at rest and used as predictors of "least absolute shrinkage and selection" (LASSO) operator regression model to estimate patients' CES-D score and to predict depressive state. RESULTS The model significantly predicted the CES-D score in all subjects (the total explained variance of CES-D score was 89.93%). Also it discriminated depressed and non-depressed CSP with 86.75% accuracy. Seven of the ten most informative metrics belonged to non-linear-domain. LIMITATIONS A higher number of patients evaluated also with a structured clinical interview would help to generalize the present findings. DISCUSSION To our knowledge this is the first study using a multi-feature approach to evaluate depression in CSP. The high informative power of HRV-nonlinear metrics suggests their possible pathophysiological role both in depression and in CHD. The high-accuracy of the algorithm at single-subject level opens to its translational use as screening tool in clinical practice.
Collapse
|
30
|
Greco A, Benvenuti SM, Gentili C, Palomba D, Valenza G, Scilingo EP. Nonlinear analysis of heart rate variability for the assessment of Dysphoria. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:3170-3173. [PMID: 29060571 DOI: 10.1109/embc.2017.8037530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Dysphoric patients show symptoms associated with Major Depression, although within a narrowed symptomatology spectrum. In prevailing practice, clinicians assess Dysphoria through psychological scores and questionnaires exclusively, therefore without taking into account objective biomarkers. In this study, we investigated heartbeat linear and nonlinear dynamics aiming to an objective assessment of Dysphoria. To this end, we derived standard and nonlinear measures from heart rate variability (HRV) series gathered from dysphoric (n=14) and nondysphoric (n=17) undergraduate students during 5 minutes of resting state. We performed both statistical and pattern recognition analyses in order to discern the two groups. Results showed significant group-wise differences in HRV nonlinear metrics exclusively, suggesting a crucial role of nonlinear sympatho-vagal dynamics in Dysphoria. Furthermore, we achieved a classification accuracy of 77.52% for the automatic identification of Dysphoria at a single-subject level.
Collapse
|
31
|
Dogan E, Sander C, Wagner X, Hegerl U, Kohls E. Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review. J Med Internet Res 2017; 19:e262. [PMID: 28739561 PMCID: PMC5547249 DOI: 10.2196/jmir.7006] [Citation(s) in RCA: 96] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 03/31/2017] [Accepted: 05/15/2017] [Indexed: 02/06/2023] Open
Abstract
Background Electronic mental health interventions for mood disorders have increased rapidly over the past decade, most recently in the form of various systems and apps that are delivered via smartphones. Objective We aim to provide an overview of studies on smartphone-based systems that combine subjective ratings with objectively measured data for longitudinal monitoring of patients with affective disorders. Specifically, we aim to examine current knowledge on: (1) the feasibility of, and adherence to, such systems; (2) the association of monitored data with mood status; and (3) the effects of monitoring on clinical outcomes. Methods We systematically searched PubMed, Web of Science, PsycINFO, and the Cochrane Central Register of Controlled Trials for relevant articles published in the last ten years (2007-2017) by applying Boolean search operators with an iterative combination of search terms, which was conducted in February 2017. Additional articles were identified via pearling, author correspondence, selected reference lists, and trial protocols. Results A total of 3463 unique records were identified. Twenty-nine studies met the inclusion criteria and were included in the review. The majority of articles represented feasibility studies (n=27); two articles reported results from one randomized controlled trial (RCT). In total, six different self-monitoring systems for affective disorders that used subjective mood ratings and objective measurements were included. These objective parameters included physiological data (heart rate variability), behavioral data (phone usage, physical activity, voice features), and context/environmental information (light exposure and location). The included articles contained results regarding feasibility of such systems in affective disorders, showed reasonable accuracy in predicting mood status and mood fluctuations based on the objectively monitored data, and reported observations about the impact of monitoring on clinical state and adherence of patients to the system usage. Conclusions The included observational studies and RCT substantiate the value of smartphone-based approaches for gathering long-term objective data (aside from self-ratings to monitor clinical symptoms) to predict changes in clinical states, and to investigate causal inferences about state changes in patients with affective disorders. Although promising, a much larger evidence-base is necessary to fully assess the potential and the risks of these approaches. Methodological limitations of the available studies (eg, small sample sizes, variations in the number of observations or monitoring duration, lack of RCT, and heterogeneity of methods) restrict the interpretability of the results. However, a number of study protocols stated ambitions to expand and intensify research in this emerging and promising field.
Collapse
Affiliation(s)
- Ezgi Dogan
- Medical Faculty, Department of Psychiatry and Psychotherapy, University Leipzig, Leipzig, Germany
| | - Christian Sander
- Medical Faculty, Department of Psychiatry and Psychotherapy, University Leipzig, Leipzig, Germany.,Depression Research Centre, German Depression Foundation, Leipzig, Germany
| | - Xenija Wagner
- Medical Faculty, Department of Psychiatry and Psychotherapy, University Leipzig, Leipzig, Germany
| | - Ulrich Hegerl
- Medical Faculty, Department of Psychiatry and Psychotherapy, University Leipzig, Leipzig, Germany.,Depression Research Centre, German Depression Foundation, Leipzig, Germany
| | - Elisabeth Kohls
- Medical Faculty, Department of Psychiatry and Psychotherapy, University Leipzig, Leipzig, Germany
| |
Collapse
|
32
|
Gentili C, Valenza G, Nardelli M, Lanatà A, Bertschy G, Weiner L, Mauri M, Scilingo EP, Pietrini P. Longitudinal monitoring of heartbeat dynamics predicts mood changes in bipolar patients: A pilot study. J Affect Disord 2017; 209:30-38. [PMID: 27870943 DOI: 10.1016/j.jad.2016.11.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Revised: 10/23/2016] [Accepted: 11/07/2016] [Indexed: 12/12/2022]
Abstract
OBJECTIVES Recent research indicates that Heart Rate Variability (HRV) is affected in Bipolar Disorders (BD) patients. To determine whether such alterations are a mere expression of the current mood state or rather contain longitudinal information on BD course, we examined the potential influence of states adjacent in time upon HRV features measured in a target mood state. METHODS Longitudinal evaluation of HRV was obtained in eight BD patients by using a wearable monitoring system developed within the PSYCHE project. We extracted time-domain, frequency-domain and non-linear HRV-features and trained a Support Vector Machine (SVM) to classify HRV-features according to mood state. To evaluate the influence of adjacent mood states, we trained SVM with different HRV-feature sets: 1) belonging to each mood state considered alone; 2) belonging to each mood state and normalized using information from the preceding mood state; 3) belonging to each mood state and normalized using information from the preceding and subsequent mood states; 4) belonging to each mood state and normalized using information from two randomly chosen states. RESULTS SVM classification accuracy within a target state was significantly greater when HRV-features from the previous and subsequent mood states were considered. CONCLUSIONS Although preliminary and in need of replications our results suggest for the first time that psychophysiological states in BD contain information related to the subsequent ones. Such characteristic may be used to improve clinical management and to develop algorithms to predict clinical course and mood switches in individual patients.
Collapse
Affiliation(s)
- Claudio Gentili
- Department of General Psychology, University of Padua, Via Venezia 8, 35139 Padua, Italy.
| | - Gaetano Valenza
- Department of Information Engineering & Research Centre "E. Piaggio", School of Engineering, University of Pisa, Italy
| | - Mimma Nardelli
- Department of Information Engineering & Research Centre "E. Piaggio", School of Engineering, University of Pisa, Italy
| | - Antonio Lanatà
- Department of Information Engineering & Research Centre "E. Piaggio", School of Engineering, University of Pisa, Italy
| | - Gilles Bertschy
- INSERM U1114, Fédération de Médecine Translationnelle de Strasbourg, Université de Strasbourg, Pôle de Psychiatrie et Santé Mentale des Hôpitaux Universitaires de Strasbourg, France
| | - Luisa Weiner
- INSERM U1114, Fédération de Médecine Translationnelle de Strasbourg, Université de Strasbourg, Pôle de Psychiatrie et Santé Mentale des Hôpitaux Universitaires de Strasbourg, France
| | - Mauro Mauri
- Section of Psychiatry, Department of Clinical and Experimental Medicine, University of Pisa, Italy
| | - Enzo Pasquale Scilingo
- Department of Information Engineering & Research Centre "E. Piaggio", School of Engineering, University of Pisa, Italy
| | - Pietro Pietrini
- IMT School for Advanced Studies, Piazza San Ponziano, 6 - 55100 Lucca, Italy.
| |
Collapse
|
33
|
A Wearable System for the Evaluation of the Human-Horse Interaction: A Preliminary Study. ELECTRONICS 2016. [DOI: 10.3390/electronics5040063] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
|
34
|
Skin Admittance Measurement for Emotion Recognition: A Study over Frequency Sweep. ELECTRONICS 2016. [DOI: 10.3390/electronics5030046] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
35
|
Valenza G, Carli V, Lanata A, Chen W, Jafari R, Scilingo EP. Guest Editorial Sensor Informatics for Managing Mental Health. IEEE J Biomed Health Inform 2016. [DOI: 10.1109/jbhi.2016.2565758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|