Speech analysis for mood state characterization in bipolar patients

Differentiation between depression and bipolar disorder in child and adolescents by voice features

OBJECTIVE

Major depressive disorder (MDD) and bipolar disorder (BD) are serious chronic disabling mental and emotional disorders, with symptoms that often manifest atypically in children and adolescents, making diagnosis difficult without objective physiological indicators. Therefore, we aimed to objectively identify MDD and BD in children and adolescents by exploring their voiceprint features.

METHODS

This study included a total of 150 participants, with 50 MDD patients, 50 BD patients, and 50 healthy controls aged between 6 and 16 years. After collecting voiceprint data, chi-square test was used to screen and extract voiceprint features specific to emotional disorders in children and adolescents. Then, selected characteristic voiceprint features were used to establish training and testing datasets with the ratio of 7:3. The performances of various machine learning and deep learning algorithms were compared using the training dataset, and the optimal algorithm was selected to classify the testing dataset and calculate the sensitivity, specificity, accuracy, and ROC curve.

RESULTS

The three groups showed differences in clustering centers for various voice features such as root mean square energy, power spectral slope, low-frequency percentile energy level, high-frequency spectral slope, spectral harmonic gain, and audio signal energy level. The model of linear SVM showed the best performance in the training dataset, achieving a total accuracy of 95.6% in classifying the three groups in the testing dataset, with sensitivity of 93.3% for MDD, 100% for BD, specificity of 93.3%, AUC of 1 for BD, and AUC of 0.967 for MDD.

CONCLUSION

By exploring the characteristics of voice features in children and adolescents, machine learning can effectively differentiate between MDD and BD in a population, and voice features hold promise as an objective physiological indicator for the auxiliary diagnosis of mood disorder in clinical practice.

Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review

BACKGROUND

Major depressive episode (MDE) is a common clinical syndrome. It can be found in different pathologies such as major depressive disorder (MDD), bipolar disorder (BD), posttraumatic stress disorder (PTSD), or even occur in the context of psychological trauma. However, only 1 syndrome is described in international classifications (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5]/International Classification of Diseases 11th Revision [ICD-11]), which do not take into account the underlying pathology at the origin of the MDE. Clinical interviews are currently the best source of information to obtain the etiological diagnosis of MDE. Nevertheless, it does not allow an early diagnosis and there are no objective measures of extracted clinical information. To remedy this, the use of digital tools and their correlation with clinical symptomatology could be useful.

OBJECTIVE

We aimed to review the current application of digital tools for MDE diagnosis while highlighting shortcomings for further research. In addition, our work was focused on digital devices easy to use during clinical interview and mental health issues where depression is common.

METHODS

We conducted a narrative review of the use of digital tools during clinical interviews for MDE by searching papers published in PubMed/MEDLINE, Web of Science, and Google Scholar databases since February 2010. The search was conducted from June to September 2021. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) automated voice analysis, behavior analysis by (2) video and physiological measures, (3) heart rate variability (HRV), and (4) electrodermal activity (EDA). For this purpose, we were interested in 4 frequently found clinical conditions in which MDE can occur: (1) MDD, (2) BD, (3) PTSD, and (4) psychological trauma.

RESULTS

A total of 74 relevant papers on the subject were qualitatively analyzed and the information was synthesized. Thus, a digital phenotype of MDE seems to emerge consisting of modifications in speech features (namely, temporal, prosodic, spectral, source, and formants) and in speech content, modifications in nonverbal behavior (head, hand, body and eyes movement, facial expressivity, and gaze), and a decrease in physiological measurements (HRV and EDA). We not only found similarities but also differences when MDE occurs in MDD, BD, PTSD, or psychological trauma. However, comparative studies were rare in BD or PTSD conditions, which does not allow us to identify clear and distinct digital phenotypes.

CONCLUSIONS

Our search identified markers from several modalities that hold promise for helping with a more objective diagnosis of MDE. To validate their potential, further longitudinal and prospective studies are needed.

Acoustic and Facial Features From Clinical Interviews for Machine Learning-Based Psychiatric Diagnosis: Algorithm Development

BACKGROUND

In contrast to all other areas of medicine, psychiatry is still nearly entirely reliant on subjective assessments such as patient self-report and clinical observation. The lack of objective information on which to base clinical decisions can contribute to reduced quality of care. Behavioral health clinicians need objective and reliable patient data to support effective targeted interventions.

OBJECTIVE

We aimed to investigate whether reliable inferences-psychiatric signs, symptoms, and diagnoses-can be extracted from audiovisual patterns in recorded evaluation interviews of participants with schizophrenia spectrum disorders and bipolar disorder.

METHODS

We obtained audiovisual data from 89 participants (mean age 25.3 years; male: 48/89, 53.9%; female: 41/89, 46.1%): individuals with schizophrenia spectrum disorders (n=41), individuals with bipolar disorder (n=21), and healthy volunteers (n=27). We developed machine learning models based on acoustic and facial movement features extracted from participant interviews to predict diagnoses and detect clinician-coded neuropsychiatric symptoms, and we assessed model performance using area under the receiver operating characteristic curve (AUROC) in 5-fold cross-validation.

RESULTS

The model successfully differentiated between schizophrenia spectrum disorders and bipolar disorder (AUROC 0.73) when aggregating face and voice features. Facial action units including cheek-raising muscle (AUROC 0.64) and chin-raising muscle (AUROC 0.74) provided the strongest signal for men. Vocal features, such as energy in the frequency band 1 to 4 kHz (AUROC 0.80) and spectral harmonicity (AUROC 0.78), provided the strongest signal for women. Lip corner-pulling muscle signal discriminated between diagnoses for both men (AUROC 0.61) and women (AUROC 0.62). Several psychiatric signs and symptoms were successfully inferred: blunted affect (AUROC 0.81), avolition (AUROC 0.72), lack of vocal inflection (AUROC 0.71), asociality (AUROC 0.63), and worthlessness (AUROC 0.61).

CONCLUSIONS

This study represents advancement in efforts to capitalize on digital data to improve diagnostic assessment and supports the development of a new generation of innovative clinical tools by employing acoustic and facial data analysis.

Voice analyses using smartphone-based data in patients with bipolar disorder, unaffected relatives and healthy control individuals, and during different affective states

BACKGROUND

Voice features have been suggested as objective markers of bipolar disorder (BD).

AIMS

To investigate whether voice features from naturalistic phone calls could discriminate between (1) BD, unaffected first-degree relatives (UR) and healthy control individuals (HC); (2) affective states within BD.

METHODS

Voice features were collected daily during naturalistic phone calls for up to 972 days. A total of 121 patients with BD, 21 UR and 38 HC were included. A total of 107.033 voice data entries were collected [BD (n  = 78.733), UR (n  = 8004), and HC (n  =  20.296)]. Daily, patients evaluated symptoms using a smartphone-based system. Affective states were defined according to these evaluations. Data were analyzed using random forest machine learning algorithms.

RESULTS

Compared to HC, BD was classified with a sensitivity of 0.79 (SD 0.11)/AUC  = 0.76 (SD 0.11) and UR with a sensitivity of 0.53 (SD 0.21)/AUC of 0.72 (SD 0.12). Within BD, compared to euthymia, mania was classified with a specificity of 0.75 (SD 0.16)/AUC  =  0.66 (SD 0.11). Compared to euthymia, depression was classified with a specificity of 0.70 (SD 0.16)/AUC  =  0.66 (SD 0.12). In all models the user dependent models outperformed the user independent models. Models combining increased mood, increased activity and insomnia compared to periods without performed best with a specificity of 0.78 (SD 0.16)/AUC  =  0.67 (SD 0.11).

CONCLUSIONS

Voice features from naturalistic phone calls may represent a supplementary objective marker discriminating BD from HC and a state marker within BD.

Speech-based markers for posttraumatic stress disorder in US veterans

BACKGROUND

The diagnosis of posttraumatic stress disorder (PTSD) is usually based on clinical interviews or self-report measures. Both approaches are subject to under- and over-reporting of symptoms. An objective test is lacking. We have developed a classifier of PTSD based on objective speech-marker features that discriminate PTSD cases from controls.

METHODS

Speech samples were obtained from warzone-exposed veterans, 52 cases with PTSD and 77 controls, assessed with the Clinician-Administered PTSD Scale. Individuals with major depressive disorder (MDD) were excluded. Audio recordings of clinical interviews were used to obtain 40,526 speech features which were input to a random forest (RF) algorithm.

RESULTS

The selected RF used 18 speech features and the receiver operating characteristic curve had an area under the curve (AUC) of 0.954. At a probability of PTSD cut point of 0.423, Youden's index was 0.787, and overall correct classification rate was 89.1%. The probability of PTSD was higher for markers that indicated slower, more monotonous speech, less change in tonality, and less activation. Depression symptoms, alcohol use disorder, and TBI did not meet statistical tests to be considered confounders.

CONCLUSIONS

This study demonstrates that a speech-based algorithm can objectively differentiate PTSD cases from controls. The RF classifier had a high AUC. Further validation in an independent sample and appraisal of the classifier to identify those with MDD only compared with those with PTSD comorbid with MDD is required.

Detecting Manic State of Bipolar Disorder Based on Support Vector Machine and Gaussian Mixture Model Using Spontaneous Speech

OBJECTIVE

This study was aimed to compare the accuracy of Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) in the detection of manic state of bipolar disorders (BD) of single patients and multiple patients.

METHODS

21 hospitalized BD patients (14 females, average age 34.5±15.3) were recruited after admission. Spontaneous speech was collected through a preloaded smartphone. Firstly, speech features [pitch, formants, mel-frequency cepstrum coefficients (MFCC), linear prediction cepstral coefficient (LPCC), gamma-tone frequency cepstral coefficients (GFCC) etc.] were preprocessed and extracted. Then, speech features were selected using the features of between-class variance and within-class variance. The manic state of patients was then detected by SVM and GMM methods.

RESULTS

LPCC demonstrated the best discrimination efficiency. The accuracy of manic state detection for single patients was much better using SVM method than GMM method. The detection accuracy for multiple patients was higher using GMM method than SVM method.

CONCLUSION

SVM provided an appropriate tool for detecting manic state for single patients, whereas GMM worked better for multiple patients' manic state detection. Both of them could help doctors and patients for better diagnosis and mood state monitoring in different situations.

Smartphone-based objective monitoring in bipolar disorder: status and considerations

In 2001, the WHO stated that: "The use of mobile and wireless technologies to support the achievement of health objectives (mHealth) has the potential to transform the face of health service delivery across the globe". Within mental health, interventions and monitoring systems for depression, anxiety, substance abuse, eating disorder, schizophrenia and bipolar disorder have been developed and used. The present paper presents the status and findings from studies using automatically generated objective smartphone data in the monitoring of bipolar disorder, and addresses considerations on the current literature and methodological as well as clinical aspects to consider in the future studies.

[Assessment of mood disorders by passive data gathering: The concept of digital phenotype versus psychiatrist's professional culture]

OBJECTIVES

The search for objective clinical signs is a constant practitioners' and researchers' concern in psychiatry. New technologies (embedded sensors, artificial intelligence) give an easier access to untapped information such as passive data (i.e. that do not require patient intervention). The concept of "digital phenotype" is emerging in psychiatry: a psychomotor alteration translated by accelerometer's modifications contrasting with the usual functioning of the subject, or the graphorrhea of patients presenting a manic episode which is replaced by an increase of SMS sent. Our main objective is to highlight the digital phenotype of mood disorders by means of a selective review of the literature.

METHOD

We conducted a selective review of the literature by querying the PubMed database until February 2017 with the terms [Computer] [Computerized] [Machine] [Automatic] [Automated] [Heart rate variability] [HRV] [actigraphy] [actimetry] [digital] [motion] [temperature] [Mood] [Bipolar] [Depression] [Depressive]. Eight hundred and forty-nine articles were submitted for evaluation, 37 articles were included.

RESULTS

For unipolar disorders, smartphones can diagnose depression with excellent accuracy by combining GPS and call log data. Actigraphic measurements showing daytime alteration in basal function while ECG sensors assessing variation in heart rate variability (HRV) and body temperature appear to be useful tools to diagnose a depressive episode. For bipolar disorders, systems which combine several sensors are described: MONARCA, PRIORI, SIMBA and PSYCHE. All these systems combine passive and active data on smartphones. From a synthesis of these data, a digital phenotype of the disorders is proposed based on the accelerometer and the GPS, the ECG, the body temperature, the use of the smartphone and the voice. This digital phenotype thus brings into question certain clinical paradigms in which psychiatrists evolve.

CONCLUSION

All these systems can be used to computerize the clinical characteristics of the various mental states studied, sometimes with greater precision than a clinician could do. Most authors recommend the use of passive data rather than active data in the context of bipolar disorders because automatically generated data reduce biases and limit the feeling of intrusion that self-questionnaires may cause. The impact of these technologies questions the psychiatrist's professional culture, defined as a specific language and a set of common values. We address issues related to these changes. Impact on psychiatrists could be important because their unity seems to be questioned due to technologies that profoundly modify the collect and process of clinical data.

Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review

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.

A Transdiagnostic Review of Negative Symptom Phenomenology and Etiology

In the DSM5, negative symptoms are 1 of the 5 core dimensions of psychopathology evaluated for schizophrenia. However, negative symptoms are not pathognomonic-they are also part of the diagnostic criteria for other schizophrenia-spectrum disorders, disorders that sometimes have comorbid psychosis, diagnoses not in the schizophrenia-spectrum, and the general "nonclinical" population. Although etiological models of negative symptoms have been developed for chronic schizophrenia, there has been little attention given to whether these models have transdiagnostic applicability. In the current review, we examine areas of commonality and divergence in the clinical presentation and etiology of negative symptoms across diagnostic categories. It was concluded that negative symptoms are relatively frequent across diagnostic categories, but individual disorders may differ in whether their negative symptoms are persistent/transient or primary/secondary. Evidence for separate dimensions of volitional and expressive symptoms exists, and there may be multiple mechanistic pathways to the same symptom phenomenon among DSM-5 disorders within and outside the schizophrenia-spectrum (ie, equifinality). Evidence for a novel transdiagnostic etiological model is presented based on the Research Domain Criteria (RDoC) constructs, which proposes the existence of 2 such pathways-a hedonic pathway and a cognitive pathway-that can both lead to expressive or volitional symptoms. To facilitate treatment breakthroughs, future transdiagnostic studies on negative symptoms are warranted that explore mechanisms underlying volitional and expressive pathology.

Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research

Psychiatric disorders are linked to a variety of biological, psychological, and contextual causes and consequences. Laboratory studies have elucidated the importance of several key physiological and behavioral biomarkers in the study of psychiatric disorders, but much less is known about the role of these biomarkers in naturalistic settings. These gaps are largely driven by methodological barriers to assessing biomarker data rapidly, reliably, and frequently outside the clinic or laboratory. Mobile health (mHealth) tools offer new opportunities to study relevant biomarkers in concert with other types of data (e.g., self-reports, global positioning system data). This review provides an overview on the state of this emerging field and describes examples from the literature where mHealth tools have been used to measure a wide array of biomarkers in the context of psychiatric functioning (e.g., psychological stress, anxiety, autism, substance use). We also outline advantages and special considerations for incorporating mHealth tools for remote biomarker measurement into studies of psychiatric illness and treatment and identify several specific opportunities for expanding this promising methodology. Integrating mHealth tools into this area may dramatically improve psychiatric science and facilitate highly personalized clinical care of psychiatric disorders.

Voice quality in patients suffering from bipolar disease

People suffering from bipolar disease are more and more common. Such pathology can severely affect patients' lifestyle by wide, and sometimes extreme, mood swings. Biosignals can be very useful to understand this disease. Specifically, speech-related features have been seen to vary in depressed people with respect to healthy subjects. Usually prosodic, spectral and energy-related features are studied. Some further information, instead, can be provided studying voice quality. According to Laver's model, voice quality is sensitive and depends on both anatomic/physiologic issues and long-term muscular adjustments of the larynx or the supraglottal vocal tract. A pilot study on both bipolar patients and healthy control subjects, performed by means of the Long-Term Average Spectrum (LTAS) is presented. The effects on LTAS estimation of a F0-correction procedure are discussed. Pairwise statistical comparisons between subjects in euthymic and depressed states and euthymic and hypomanic states were performed. Significant differences were found in some frequency intervals in both cases. The F0-correction procedure modified the values of the significant frequency intervals in the euthymic/depressed comparison, that also was characterized by a change of F0. Noticeably, no statistically significant differences were found in control subjects acquired in the same mood state. Though the number of subjects is small, the results are encouraging given their coherence across patients and the lack of differences in the control group. Finally, this work suggests that particular vocal settings might be involved in different mood states.

Development and Evaluation of a Smartphone-Based Measure of Social Rhythms for Bipolar Disorder

Dynamic psychological processes are most often assessed using self-report instruments. This places a constraint on how often and for how long data can be collected due to the burden placed on human participants. Smartphones are ubiquitous and highly personal devices, equipped with sensors that offer an opportunity to measure and understand psychological processes in real-world contexts over the long term. In this article, we present a novel smartphone approach to address the limitations of self-report in bipolar disorder where mood and activity are key constructs. We describe the development of MoodRhythm, a smartphone application that incorporates existing self-report elements from interpersonal and social rhythm therapy, a clinically validated treatment, and combines them with novel inputs from smartphone sensors. We reflect on lessons learned in transitioning from an existing self-report instrument to one that involves smartphone sensors and discuss the potential impact of these changes on the future of psychological assessment.

Voice analysis as an objective state marker in bipolar disorder

Changes in speech have been suggested as sensitive and valid measures of depression and mania in bipolar disorder. The present study aimed at investigating (1) voice features collected during phone calls as objective markers of affective states in bipolar disorder and (2) if combining voice features with automatically generated objective smartphone data on behavioral activities (for example, number of text messages and phone calls per day) and electronic self-monitored data (mood) on illness activity would increase the accuracy as a marker of affective states. Using smartphones, voice features, automatically generated objective smartphone data on behavioral activities and electronic self-monitored data were collected from 28 outpatients with bipolar disorder in naturalistic settings on a daily basis during a period of 12 weeks. Depressive and manic symptoms were assessed using the Hamilton Depression Rating Scale 17-item and the Young Mania Rating Scale, respectively, by a researcher blinded to smartphone data. Data were analyzed using random forest algorithms. Affective states were classified using voice features extracted during everyday life phone calls. Voice features were found to be more accurate, sensitive and specific in the classification of manic or mixed states with an area under the curve (AUC)=0.89 compared with an AUC=0.78 for the classification of depressive states. Combining voice features with automatically generated objective smartphone data on behavioral activities and electronic self-monitored data increased the accuracy, sensitivity and specificity of classification of affective states slightly. Voice features collected in naturalistic settings using smartphones may be used as objective state markers in patients with bipolar disorder.

MOOD STATE PREDICTION FROM SPEECH OF VARYING ACOUSTIC QUALITY FOR INDIVIDUALS WITH BIPOLAR DISORDER

Speech contains patterns that can be altered by the mood of an individual. There is an increasing focus on automated and distributed methods to collect and monitor speech from large groups of patients suffering from mental health disorders. However, as the scope of these collections increases, the variability in the data also increases. This variability is due in part to the range in the quality of the devices, which in turn affects the quality of the recorded data, negatively impacting the accuracy of automatic assessment. It is necessary to mitigate variability effects in order to expand the impact of these technologies. This paper explores speech collected from phone recordings for analysis of mood in individuals with bipolar disorder. Two different phones with varying amounts of clipping, loudness, and noise are employed. We describe methodologies for use during preprocessing, feature extraction, and data modeling to correct these differences and make the devices more comparable. The results demonstrate that these pipeline modifications result in statistically significantly higher performance, which highlights the potential of distributed mental health systems.

Internet-based psychological interventions for bipolar disorder: Review of the present and insights into the future

BACKGROUND

In the last decade, there has been an increasing advent of innovative concepts in psychological interventions aimed at empowering bipolar patients by means of technological advancements and taking advantage of the proliferation of the Internet. Since the adoption of these technologies for behavioral monitoring and intervention is not trivial in clinical practice, the main objective of this review is to provide an overview and to discuss the several initiatives published so far in the literature related to the Internet-based technologies aimed to deliver evidence-based psychological interventions for bipolar disorder patients.

METHODS

We conducted a comprehensive systematic review of the literature from multiple technological, psychiatric and psychological domains. The search was conducted by applying the Boolean algorithm "BIPOLAR AND DISORDER AND (treatment OR intervention) AND (online OR Internet OR web-based OR smartphone OR mobile)" at MEDLINE, SCOPUS, EMBASE, ClinicalTrials, ISI Web of Science and Google Scholar.

RESULTS

We identified over 251 potential entries matching the search criteria and after a thorough manual review, 29 publications pertaining to 12 different projects, specifically focusing on psychological interventions for bipolar patients through diverse Internet-based methods, were selected.

LIMITATIONS

Taking into consideration the diversity of the initiatives and the inconclusive main outcome results of the studies, there is still limited evidence available to draw firm conclusions about the efficacy of interventions using Internet-based technologies for bipolar disorder.

CONCLUSIONS

However, considering the high rates of retention and compliance reported, they represent a potential highly feasible and acceptable method of delivering this kind of interventions to bipolar patients.

Smartphone Application for the Analysis of Prosodic Features in Running Speech with a Focus on Bipolar Disorders: System Performance Evaluation and Case Study

Bipolar disorder is one of the most common mood disorders characterized by large and invalidating mood swings. Several projects focus on the development of decision support systems that monitor and advise patients, as well as clinicians. Voice monitoring and speech signal analysis can be exploited to reach this goal. In this study, an Android application was designed for analyzing running speech using a smartphone device. The application can record audio samples and estimate speech fundamental frequency, F0, and its changes. F0-related features are estimated locally on the smartphone, with some advantages with respect to remote processing approaches in terms of privacy protection and reduced upload costs. The raw features can be sent to a central server and further processed. The quality of the audio recordings, algorithm reliability and performance of the overall system were evaluated in terms of voiced segment detection and features estimation. The results demonstrate that mean F0 from each voiced segment can be reliably estimated, thus describing prosodic features across the speech sample. Instead, features related to F0 variability within each voiced segment performed poorly. A case study performed on a bipolar patient is presented.

Electrodermal activity in bipolar patients during affective elicitation

Bipolar patients are characterized by a pathological unpredictable behavior, resulting in fluctuations between states of depression and episodes of mania or hypomania. In the current clinical practice, the psychiatric diagnosis is made through clinician-administered rating scales and questionnaires, disregarding the potential contribution provided by physiological signs. The aim of this paper is to investigate how changes in the autonomic nervous system activity can be correlated with clinical mood swings. More specifically, a group of ten bipolar patients underwent an emotional elicitation protocol to investigate the autonomic nervous system dynamics, through the electrodermal activity (EDA), among different mood states. In addition, a control group of ten healthy subjects were recruited and underwent the same protocol. Physiological signals were analyzed by applying the deconvolutive method to reconstruct EDA tonic and phasic components, from which several significant features were extracted to quantify the sympathetic activation. Experimental results performed on both the healthy subjects and the bipolar patients supported the hypothesis of a relationship between autonomic dysfunctions and pathological mood states.

Telemonitoring with respect to mood disorders and information and communication technologies: overview and presentation of the PSYCHE project

This paper reviews what we know about prediction in relation to mood disorders from the perspective of clinical, biological, and physiological markers. It then also presents how information and communication technologies have developed in the field of mood disorders, from the first steps, for example, the transition from paper and pencil to more sophisticated methods, to the development of ecological momentary assessment methods and, more recently, wearable systems. These recent developments have paved the way for the use of integrative approaches capable of assessing multiple variables. The PSYCHE project stands for Personalised monitoring SYstems for Care in mental HEalth.