1
|
Saraei M, Paxton A, Xygalatas D. Aligned bodies, united hearts: embodied emotional dynamics of an Islamic ritual. Philos Trans R Soc Lond B Biol Sci 2024; 379:20230162. [PMID: 39155713 PMCID: PMC11391295 DOI: 10.1098/rstb.2023.0162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 02/14/2024] [Accepted: 02/15/2024] [Indexed: 08/20/2024] Open
Abstract
Collective rituals involve the coordination of intentions and actions and have been shown to promote the alignment of emotional states and social identities. However, the mechanics of achieving group-level synchrony is yet unclear. We report the results of a naturalistic study in the context of an Islamic congregational prayer that involves synchronous movement. We used wearable devices to capture data on body posture, autonomic responses and spatial proximity to investigate how postural alignment and shared arousal intertwine during this ritual. The findings reveal a dual process at play: postural alignment appears to be more localized, with worshippers synchronizing their movements with their nearest neighbours, while physiological alignment operates on a broader scale, primarily driven by the central role of the religious leader. Our findings underscore the importance of interpersonal dynamics in collective gatherings and the role of physical co-presence in fostering connections among participants, with implications extending to our understanding of group dynamics across various social settings.This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.
Collapse
Affiliation(s)
- Mohammadamin Saraei
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
- Center for the Ecological Study of Perception and Action, University of Connecticut, Storrs, CT, USA
| | - Alexandra Paxton
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
- Center for the Ecological Study of Perception and Action, University of Connecticut, Storrs, CT, USA
| | - Dimitris Xygalatas
- Department of Psychological Sciences, University of Connecticut, Storrs, CT, USA
- Center for the Ecological Study of Perception and Action, University of Connecticut, Storrs, CT, USA
- Department of Anthropology, University of Connecticut, Storrs, CT, USA
| |
Collapse
|
2
|
Roßkopf S, Meder B. [Healthcare 4.0-Medicine in transition]. Herz 2024; 49:350-354. [PMID: 39115627 DOI: 10.1007/s00059-024-05267-w] [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] [Accepted: 07/17/2024] [Indexed: 09/26/2024]
Abstract
Healthcare 4.0 describes the future transformation of the healthcare sector driven by the combination of digital technologies, such as artificial intelligence (AI), big data and the Internet of Medical Things, enabling the advancement of precision medicine. This overview article addresses various areas such as large language models (LLM), diagnostics and robotics, shedding light on the positive aspects of Healthcare 4.0 and showcasing exciting methods and application examples in cardiology. It delves into the broad knowledge base and enormous potential of LLMs, highlighting their immediate benefits as digital assistants or for administrative tasks. In diagnostics, the increasing usefulness of wearables is emphasized and an AI for predicting heart filling pressures based on cardiac magnetic resonance imaging (MRI) is introduced. Additionally, it discusses the revolutionary methodology of a digital simulation of the physical heart (digital twin). Finally, it addresses both regulatory frameworks and a brief vision of data-driven healthcare delivery, explaining the need for investments in technical personnel and infrastructure to achieve a more effective medicine.
Collapse
Affiliation(s)
- Steffen Roßkopf
- Precision Digital Health and Informatics for Life, Clinic of Internal Medicine III, University of Heidelberg, 69120, Heidelberg, Deutschland
- Clinic of Internal Medicine III and Department of Cardiology, Angiology and Pulmology, Center of Internal Medicine, University of Heidelberg, Heidelberg, Deutschland
| | - Benjamin Meder
- Precision Digital Health and Informatics for Life, Clinic of Internal Medicine III, University of Heidelberg, 69120, Heidelberg, Deutschland.
- Clinic of Internal Medicine III and Department of Cardiology, Angiology and Pulmology, Center of Internal Medicine, University of Heidelberg, Heidelberg, Deutschland.
- eCardiology, German Cardiac Society, Düsseldorf, Deutschland.
| |
Collapse
|
3
|
Ortiz BL, Gupta V, Kumar R, Jalin A, Cao X, Ziegenbein C, Singhal A, Tewari M, Choi SW. Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care. JMIR Mhealth Uhealth 2024; 12:e59587. [PMID: 38626290 PMCID: PMC11470224 DOI: 10.2196/59587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/12/2024] [Accepted: 08/27/2024] [Indexed: 04/18/2024] Open
Abstract
BACKGROUND Wearable sensors are increasingly being explored in health care, including in cancer care, for their potential in continuously monitoring patients. Despite their growing adoption, significant challenges remain in the quality and consistency of data collected from wearable sensors. Moreover, preprocessing pipelines to clean, transform, normalize, and standardize raw data have not yet been fully optimized. OBJECTIVE This study aims to conduct a scoping review of preprocessing techniques used on raw wearable sensor data in cancer care, specifically focusing on methods implemented to ensure their readiness for artificial intelligence and machine learning (AI/ML) applications. We sought to understand the current landscape of approaches for handling issues, such as noise, missing values, normalization or standardization, and transformation, as well as techniques for extracting meaningful features from raw sensor outputs and converting them into usable formats for subsequent AI/ML analysis. METHODS We systematically searched IEEE Xplore, PubMed, Embase, and Scopus to identify potentially relevant studies for this review. The eligibility criteria included (1) mobile health and wearable sensor studies in cancer, (2) written and published in English, (3) published between January 2018 and December 2023, (4) full text available rather than abstracts, and (5) original studies published in peer-reviewed journals or conferences. RESULTS The initial search yielded 2147 articles, of which 20 (0.93%) met the inclusion criteria. Three major categories of preprocessing techniques were identified: data transformation (used in 12/20, 60% of selected studies), data normalization and standardization (used in 8/20, 40% of the selected studies), and data cleaning (used in 8/20, 40% of the selected studies). Transformation methods aimed to convert raw data into more informative formats for analysis, such as by segmenting sensor streams or extracting statistical features. Normalization and standardization techniques usually normalize the range of features to improve comparability and model convergence. Cleaning methods focused on enhancing data reliability by handling artifacts like missing values, outliers, and inconsistencies. CONCLUSIONS While wearable sensors are gaining traction in cancer care, realizing their full potential hinges on the ability to reliably translate raw outputs into high-quality data suitable for AI/ML applications. This review found that researchers are using various preprocessing techniques to address this challenge, but there remains a lack of standardized best practices. Our findings suggest a pressing need to develop and adopt uniform data quality and preprocessing workflows of wearable sensor data that can support the breadth of cancer research and varied patient populations. Given the diverse preprocessing techniques identified in the literature, there is an urgency for a framework that can guide researchers and clinicians in preparing wearable sensor data for AI/ML applications. For the scoping review as well as our research, we propose a general framework for preprocessing wearable sensor data, designed to be adaptable across different disease settings, moving beyond cancer care.
Collapse
Affiliation(s)
- Bengie L Ortiz
- Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States
| | - Vibhuti Gupta
- School of Applied Computational Sciences, Meharry Medical College, Nashville, TN, United States
| | - Rajnish Kumar
- Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States
| | - Aditya Jalin
- Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States
| | - Xiao Cao
- Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States
| | - Charles Ziegenbein
- Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States
- Autonomous Systems Research Department, Peraton Labs, Basking Ridge, NJ, United States
| | - Ashutosh Singhal
- School of Applied Computational Sciences, Meharry Medical College, Nashville, TN, United States
| | - Muneesh Tewari
- Department of Biomedical Engineering, College of Engineering, University of Michigan, Ann Arbor, MI, United States
- Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI, United States
- VA Ann Arbor Healthcare System, Ann Arbor, MI, United States
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Sung Won Choi
- Department of Pediatrics, Hematology and Oncology Division, Michigan Medicine, University of Michigan Health System, Ann Arbor, MI, United States
- Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI, United States
| |
Collapse
|
4
|
Gaur P, Temple DS, Hegarty-Craver M, Boyce MD, Holt JR, Wenger MF, Preble EA, Eckhoff RP, McCombs MS, Davis-Wilson HC, Walls HJ, Dausch DE. Continuous Monitoring of Heart Rate Variability in Free-Living Conditions Using Wearable Sensors: Exploratory Observational Study. JMIR Form Res 2024; 8:e53977. [PMID: 39110968 PMCID: PMC11339560 DOI: 10.2196/53977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 05/14/2024] [Accepted: 06/06/2024] [Indexed: 08/25/2024] Open
Abstract
BACKGROUND Wearable physiological monitoring devices are promising tools for remote monitoring and early detection of potential health changes of interest. The widespread adoption of such an approach across communities and over long periods of time will require an automated data platform for collecting, processing, and analyzing relevant health information. OBJECTIVE In this study, we explore prospective monitoring of individual health through an automated data collection, metrics extraction, and health anomaly analysis pipeline in free-living conditions over a continuous monitoring period of several months with a focus on viral respiratory infections, such as influenza or COVID-19. METHODS A total of 59 participants provided smartwatch data and health symptom and illness reports daily over an 8-month window. Physiological and activity data from photoplethysmography sensors, including high-resolution interbeat interval (IBI) and step counts, were uploaded directly from Garmin Fenix 6 smartwatches and processed automatically in the cloud using a stand-alone, open-source analytical engine. Health risk scores were computed based on a deviation in heart rate and heart rate variability metrics from each individual's activity-matched baseline values, and scores exceeding a predefined threshold were checked for corresponding symptoms or illness reports. Conversely, reports of viral respiratory illnesses in health survey responses were also checked for corresponding changes in health risk scores to qualitatively assess the risk score as an indicator of acute respiratory health anomalies. RESULTS The median average percentage of sensor data provided per day indicating smartwatch wear compliance was 70%, and survey responses indicating health reporting compliance was 46%. A total of 29 elevated health risk scores were detected, of which 12 (41%) had concurrent survey data and indicated a health symptom or illness. A total of 21 influenza or COVID-19 illnesses were reported by study participants; 9 (43%) of these reports had concurrent smartwatch data, of which 6 (67%) had an increase in health risk score. CONCLUSIONS We demonstrate a protocol for data collection, extraction of heart rate and heart rate variability metrics, and prospective analysis that is compatible with near real-time health assessment using wearable sensors for continuous monitoring. The modular platform for data collection and analysis allows for a choice of different wearable sensors and algorithms. Here, we demonstrate its implementation in the collection of high-fidelity IBI data from Garmin Fenix 6 smartwatches worn by individuals in free-living conditions, and the prospective, near real-time analysis of the data, culminating in the calculation of health risk scores. To our knowledge, this study demonstrates for the first time the feasibility of measuring high-resolution heart IBI and step count using smartwatches in near real time for respiratory illness detection over a long-term monitoring period in free-living conditions.
Collapse
Affiliation(s)
- Pooja Gaur
- Research Triangle Institute, Research Triangle Park, NC, United States
| | - Dorota S Temple
- Research Triangle Institute, Research Triangle Park, NC, United States
| | | | - Matthew D Boyce
- Research Triangle Institute, Research Triangle Park, NC, United States
| | - Jonathan R Holt
- Research Triangle Institute, Research Triangle Park, NC, United States
| | - Michael F Wenger
- Research Triangle Institute, Research Triangle Park, NC, United States
| | - Edward A Preble
- Research Triangle Institute, Research Triangle Park, NC, United States
| | - Randall P Eckhoff
- Research Triangle Institute, Research Triangle Park, NC, United States
| | | | | | - Howard J Walls
- Research Triangle Institute, Research Triangle Park, NC, United States
| | - David E Dausch
- Research Triangle Institute, Research Triangle Park, NC, United States
| |
Collapse
|
5
|
Mohammed A, Li S, Liu X. Exploring the Potentials of Wearable Technologies in Managing Vestibular Hypofunction. Bioengineering (Basel) 2024; 11:641. [PMID: 39061723 PMCID: PMC11274252 DOI: 10.3390/bioengineering11070641] [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: 05/04/2024] [Revised: 05/26/2024] [Accepted: 05/31/2024] [Indexed: 07/28/2024] Open
Abstract
The vestibular system is dedicated to gaze stabilization, postural balance, and spatial orientation; this makes vestibular function crucial for our ability to interact effectively with our environment. Vestibular hypofunction (VH) progresses over time, and it presents differently in its early and advanced stages. In the initial stages of VH, the effects of VH are mitigated using vestibular rehabilitation therapy (VRT), which can be facilitated with the aid of technology. At more advanced stages of VH, novel techniques that use wearable technologies for sensory augmentation and sensory substitution have been applied to manage VH. Despite this, the potential of assistive technologies for VH management remains underexplored over the past decades. Hence, in this review article, we present the state-of-the-art technologies for facilitating early-stage VRT and for managing advanced-stage VH. Also, challenges and strategies on how these technologies can be improved to enable long-term ambulatory and home use are presented.
Collapse
Affiliation(s)
- Ameer Mohammed
- School of Information Science and Technology, Fudan University, Shanghai 200433, China; (A.M.); (S.L.)
- State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, China
| | - Shutong Li
- School of Information Science and Technology, Fudan University, Shanghai 200433, China; (A.M.); (S.L.)
- State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, China
| | - Xiao Liu
- School of Information Science and Technology, Fudan University, Shanghai 200433, China; (A.M.); (S.L.)
- State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, China
| |
Collapse
|
6
|
Hassan J, Saeed SM, Deka L, Uddin MJ, Das DB. Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges. Pharmaceutics 2024; 16:260. [PMID: 38399314 PMCID: PMC10892549 DOI: 10.3390/pharmaceutics16020260] [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: 12/08/2023] [Revised: 01/29/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024] Open
Abstract
The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.
Collapse
Affiliation(s)
- Jasmin Hassan
- Drug Delivery & Therapeutics Lab, Dhaka 1212, Bangladesh; (J.H.); (S.M.S.)
| | | | - Lipika Deka
- Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK;
| | - Md Jasim Uddin
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Diganta B. Das
- Department of Chemical Engineering, Loughborough University, Loughborough LE11 3TU, UK
| |
Collapse
|
7
|
Vuong C, Utkarsh K, Stojancic R, Subramaniam A, Fernandez O, Banerjee T, Abrams DM, Fijnvandraat K, Shah N. Use of consumer wearables to monitor and predict pain in patients with sickle cell disease. Front Digit Health 2023; 5:1285207. [PMID: 37954032 PMCID: PMC10634543 DOI: 10.3389/fdgth.2023.1285207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 10/11/2023] [Indexed: 11/14/2023] Open
Abstract
Background In sickle cell disease (SCD), unpredictable episodes of acute severe pain, known as vaso-occlusive crises (VOC), disrupt school, work activities and family life and ultimately lead to multiple hospitalizations. The ability to predict VOCs would allow a timely and adequate intervention. The first step towards this ultimate goal is to use patient-friendly and accessible technology to collect relevant data that helps infer a patient's pain experience during VOC. This study aims to: (1) determine the feasibility of remotely monitoring with a consumer wearable during hospitalization for VOC and up to 30 days after discharge, and (2) evaluate the accuracy of pain prediction using machine learning models based on physiological parameters measured by a consumer wearable. Methods Patients with SCD (≥18 years) who were admitted for a vaso-occlusive crisis were enrolled at a single academic center. Participants were instructed to report daily pain scores (0-10) in a mobile app (Nanbar) and to continuously wear an Apple Watch up to 30 days after discharge. Data included heart rate (in rest, average and variability) and step count. Demographics, SCD genotype, and details of hospitalization including pain scores reported to nurses, were extracted from electronic medical records. Physiological data from the wearable were associated with pain scores to fit 3 different machine learning classification models. The performance of the machine learning models was evaluated using: accuracy, F1, root-mean-square error and area under the receiver-operating curve. Results Between April and June 2022, 19 patients (74% HbSS genotype) were included in this study and followed for a median time of 28 days [IQR 22-34], yielding a dataset of 2,395 pain data points. Ten participants were enrolled while hospitalized for VOC. The metrics of the best performing model, the random forest model, were micro-averaged accuracy of 92%, micro-averaged F1-score of 0.63, root-mean-square error of 1.1, and area under the receiving operating characteristic curve of 0.9. Conclusion Our random forest model accurately predicts high pain scores during admission for VOC and after discharge. The Apple Watch was a feasible method to collect physiologic data and provided accuracy in prediction of pain scores.
Collapse
Affiliation(s)
- Caroline Vuong
- Department of Pediatric Hematology, Emma Children’s Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Kumar Utkarsh
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, United States
| | - Rebecca Stojancic
- Division of Hematology—Duke Sickle Cell Comprehensive Care Unit, Department of Medicine, Duke University Hospital, Durham, NC, United States
| | - Arvind Subramaniam
- Division of Hematology—Duke Sickle Cell Comprehensive Care Unit, Department of Medicine, Duke University Hospital, Durham, NC, United States
| | - Olivia Fernandez
- Division of Hematology—Duke Sickle Cell Comprehensive Care Unit, Department of Medicine, Duke University Hospital, Durham, NC, United States
| | - Tanvi Banerjee
- Department of Computer Science & Engineering, Wright State University, Dayton, OH, United States
| | - Daniel M. Abrams
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, United States
| | - Karin Fijnvandraat
- Department of Pediatric Hematology, Emma Children’s Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Nirmish Shah
- Division of Hematology—Duke Sickle Cell Comprehensive Care Unit, Department of Medicine, Duke University Hospital, Durham, NC, United States
| |
Collapse
|
8
|
van Rossum MC, da Silva PMA, Wang Y, Kouwenhoven EA, Hermens HJ. Missing data imputation techniques for wireless continuous vital signs monitoring. J Clin Monit Comput 2023; 37:1387-1400. [PMID: 36729298 PMCID: PMC9893204 DOI: 10.1007/s10877-023-00975-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 01/16/2023] [Indexed: 02/03/2023]
Abstract
Wireless vital signs sensors are increasingly used for remote patient monitoring, but data analysis is often challenged by missing data periods. This study explored the performance of various imputation techniques for continuous vital signs measurements. Wireless vital signs measurements (heart rate, respiratory rate, blood oxygen saturation, axillary temperature) from surgical ward patients were used for repeated random simulation of missing data periods (gaps) of 5-60 min in two-hour windows. Gaps were imputed using linear interpolation, spline interpolation, last observation- and mean carried forwards technique, and cluster-based prognosis. Imputation performance was evaluated using the mean absolute error (MAE) between original and imputed gap samples. Besides, effects on signal features (window's slope, mean) and early warning scores (EWS) were explored. Gaps were simulated in 1743 data windows, obtained from 52 patients. Although MAE ranges overlapped, median MAE was structurally lowest for linear interpolation (heart rate: 0.9-2.6 beats/min, respiratory rate: 0.8-1.8 breaths/min, temperature: 0.04-0.17 °C, oxygen saturation: 0.3-0.7% for 5-60 min gaps) but up to twice as high for other techniques. Three techniques resulted in larger ranges of signal feature bias compared to no imputation. Imputation led to EWS misclassification in 1-8% of all simulations. Imputation error ranges vary between imputation techniques and increase with gap length. Imputation may result in larger signal feature bias compared to performing no imputation, and can affect patient risk assessment as illustrated by the EWS. Accordingly, careful implementation and selection of imputation techniques is warranted.
Collapse
Affiliation(s)
- Mathilde C van Rossum
- Biomedical Signals and Systems, University of Twente, Enschede, The Netherlands.
- Cardiovascular and Respiratory Physiology, University of Twente, Postbox 217, 7500 AE, Enschede, The Netherlands.
- Department of Surgery, Hospital Group Twente, Almelo, The Netherlands.
| | - Pedro M Alves da Silva
- Biomedical Signals and Systems, University of Twente, Enschede, The Netherlands
- NOVA School of Science and Technology, NOVA University of Lisbon, Lisbon, Portugal
| | - Ying Wang
- Biomedical Signals and Systems, University of Twente, Enschede, The Netherlands
- ZGT Academy, Hospital group Twente, Almelo, The Netherlands
| | | | - Hermie J Hermens
- Biomedical Signals and Systems, University of Twente, Enschede, The Netherlands
| |
Collapse
|
9
|
Żyliński M, Nassibi A, Mandic DP. Design and Implementation of an Atrial Fibrillation Detection Algorithm on the ARM Cortex-M4 Microcontroller. SENSORS (BASEL, SWITZERLAND) 2023; 23:7521. [PMID: 37687975 PMCID: PMC10490693 DOI: 10.3390/s23177521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 08/25/2023] [Accepted: 08/26/2023] [Indexed: 09/10/2023]
Abstract
At present, a medium-level microcontroller is capable of performing edge computing and can handle the computation of neural network kernel functions. This makes it possible to implement a complete end-to-end solution incorporating signal acquisition, digital signal processing, and machine learning for the classification of cardiac arrhythmias on a small wearable device. In this work, we describe the design and implementation of several classifiers for atrial fibrillation detection on a general-purpose ARM Cortex-M4 microcontroller. We used the CMSIS-DSP library, which supports Naïve Bayes and Support Vector Machine classifiers, with different kernel functions. We also developed Python scripts to automatically transfer the Python model (trained in Scikit-learn) to the C environment. To train and evaluate the models, we used part of the data from the PhysioNet/Computing in Cardiology Challenge 2020 and performed simple classification of atrial fibrillation based on heart-rate irregularity. The performance of the classifiers was tested on a general-purpose ARM Cortex-M4 microcontroller (STM32WB55RG). Our study reveals that among the tested classifiers, the SVM classifier with RBF kernel function achieves the highest accuracy of 96.9%, sensitivity of 98.4%, and specificity of 95.8%. The execution time of this classifier was 720 μs per recording. We also discuss the advantages of moving computing tasks to edge devices, including increased power efficiency of the system, improved patient data privacy and security, and reduced overall system operation costs. In addition, we highlight a problem with false-positive detection and unclear significance of device-detected atrial fibrillation.
Collapse
Affiliation(s)
- Marek Żyliński
- Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK; (A.N.); (D.P.M.)
| | | | | |
Collapse
|
10
|
Saputra J, Lawrencya C, Saini JM, Suharjito S. Hyperparameter optimization for cardiovascular disease data-driven prognostic system. Vis Comput Ind Biomed Art 2023; 6:16. [PMID: 37524951 PMCID: PMC10390457 DOI: 10.1186/s42492-023-00143-6] [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: 03/15/2023] [Accepted: 07/04/2023] [Indexed: 08/02/2023] Open
Abstract
Prediction and diagnosis of cardiovascular diseases (CVDs) based, among other things, on medical examinations and patient symptoms are the biggest challenges in medicine. About 17.9 million people die from CVDs annually, accounting for 31% of all deaths worldwide. With a timely prognosis and thorough consideration of the patient's medical history and lifestyle, it is possible to predict CVDs and take preventive measures to eliminate or control this life-threatening disease. In this study, we used various patient datasets from a major hospital in the United States as prognostic factors for CVD. The data was obtained by monitoring a total of 918 patients whose criteria for adults were 28-77 years old. In this study, we present a data mining modeling approach to analyze the performance, classification accuracy and number of clusters on Cardiovascular Disease Prognostic datasets in unsupervised machine learning (ML) using the Orange data mining software. Various techniques are then used to classify the model parameters, such as k-nearest neighbors, support vector machine, random forest, artificial neural network (ANN), naïve bayes, logistic regression, stochastic gradient descent (SGD), and AdaBoost. To determine the number of clusters, various unsupervised ML clustering methods were used, such as k-means, hierarchical, and density-based spatial clustering of applications with noise clustering. The results showed that the best model performance analysis and classification accuracy were SGD and ANN, both of which had a high score of 0.900 on Cardiovascular Disease Prognostic datasets. Based on the results of most clustering methods, such as k-means and hierarchical clustering, Cardiovascular Disease Prognostic datasets can be divided into two clusters. The prognostic accuracy of CVD depends on the accuracy of the proposed model in determining the diagnostic model. The more accurate the model, the better it can predict which patients are at risk for CVD.
Collapse
Affiliation(s)
- Jayson Saputra
- Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia.
| | - Cindy Lawrencya
- Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia
| | - Jecky Mitra Saini
- Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia
| | - Suharjito Suharjito
- Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia
| |
Collapse
|
11
|
Staffini A, Svensson T, Chung UI, Svensson AK. A Disentangled VAE-BiLSTM Model for Heart Rate Anomaly Detection. Bioengineering (Basel) 2023; 10:683. [PMID: 37370614 PMCID: PMC10294855 DOI: 10.3390/bioengineering10060683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/19/2023] [Accepted: 06/01/2023] [Indexed: 06/29/2023] Open
Abstract
Cardiovascular diseases (CVDs) remain a leading cause of death globally. According to the American Heart Association, approximately 19.1 million deaths were attributed to CVDs in 2020, in particular, ischemic heart disease and stroke. Several known risk factors for CVDs include smoking, alcohol consumption, lack of regular physical activity, and diabetes. The last decade has been characterized by widespread diffusion in the use of wristband-style wearable devices which can monitor and collect heart rate data, among other information. Wearable devices allow the analysis and interpretation of physiological and activity data obtained from the wearer and can therefore be used to monitor and prevent potential CVDs. However, these data are often provided in a manner that does not allow the general user to immediately comprehend possible health risks, and often require further analytics to draw meaningful conclusions. In this paper, we propose a disentangled variational autoencoder (β-VAE) with a bidirectional long short-term memory network (BiLSTM) backend to detect in an unsupervised manner anomalies in heart rate data collected during sleep time with a wearable device from eight heterogeneous participants. Testing was performed on the mean heart rate sampled both at 30 s and 1 min intervals. We compared the performance of our model with other well-known anomaly detection algorithms, and we found that our model outperformed them in almost all considered scenarios and for all considered participants. We also suggest that wearable devices may benefit from the integration of anomaly detection algorithms, in an effort to provide users more processed and straightforward information.
Collapse
Affiliation(s)
- Alessio Staffini
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- Advanced Technology Department, ALBERT Inc., Shinjuku Front Tower 15F, 2-21-1, Kita-Shinjuku, Shinjuku-ku, Tokyo 169-0074, Japan
- Department of Economics and Finance, Catholic University of Milan, Largo Gemelli 1, 20123 Milan, Italy
| | - Thomas Svensson
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- Graduate School of Health Innovation, Kanagawa University of Human Services, Research Gate Building Tonomachi 2-A 2, 3F, 3-25-10 Tonomachi, Kawasaki-ku, Kawasaki-shi 210-0821, Japan
- Department of Clinical Sciences, Skåne University Hospital, Lund University, 205 02 Malmö, Sweden
| | - Ung-il Chung
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- Graduate School of Health Innovation, Kanagawa University of Human Services, Research Gate Building Tonomachi 2-A 2, 3F, 3-25-10 Tonomachi, Kawasaki-ku, Kawasaki-shi 210-0821, Japan
- Clinical Biotechnology, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Akiko Kishi Svensson
- Precision Health, Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan; (A.S.); (U.-i.C.); (A.K.S.)
- Department of Clinical Sciences, Skåne University Hospital, Lund University, 205 02 Malmö, Sweden
- Department of Diabetes and Metabolic Diseases, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| |
Collapse
|
12
|
Anda-Duran ID, Hwang PH, Popp ZT, Low S, Ding H, Rahman S, Igwe A, Kolachalama VB, Lin H, Au R. Matching science to reality: how to deploy a participant-driven digital brain health platform. FRONTIERS IN DEMENTIA 2023; 2:1135451. [PMID: 38706716 PMCID: PMC11067045 DOI: 10.3389/frdem.2023.1135451] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Introduction Advances in digital technologies for health research enable opportunities for digital phenotyping of individuals in research and clinical settings. Beyond providing opportunities for advanced data analytics with data science and machine learning approaches, digital technologies offer solutions to several of the existing barriers in research practice that have resulted in biased samples. Methods A participant-driven, precision brain health monitoring digital platform has been introduced to two longitudinal cohort studies, the Boston University Alzheimer's Disease Research Center (BU ADRC) and the Bogalusa Heart Study (BHS). The platform was developed with prioritization of digital data in native format, multiple OS, validity of derived metrics, feasibility and usability. A platform including nine remote technologies and three staff-guided digital assessments has been introduced in the BU ADRC population, including a multimodal smartphone application also introduced to the BHS population. Participants select which technologies they would like to use and can manipulate their personal platform and schedule over time. Results Participants from the BU ADRC are using an average of 5.9 technologies to date, providing strong evidence for the usability of numerous digital technologies in older adult populations. Broad phenotyping of both cohorts is ongoing, with the collection of data spanning cognitive testing, sleep, physical activity, speech, motor activity, cardiovascular health, mood, gait, balance, and more. Several challenges in digital phenotyping implementation in the BU ADRC and the BHS have arisen, and the protocol has been revised and optimized to minimize participant burden while sustaining participant contact and support. Discussion The importance of digital data in its native format, near real-time data access, passive participant engagement, and availability of technologies across OS has been supported by the pattern of participant technology use and adherence across cohorts. The precision brain health monitoring platform will be iteratively adjusted and improved over time. The pragmatic study design enables multimodal digital phenotyping of distinct clinically characterized cohorts in both rural and urban U.S. settings.
Collapse
Affiliation(s)
- Ileana De Anda-Duran
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | - Phillip H. Hwang
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
| | - Zachary Thomas Popp
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Spencer Low
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Huitong Ding
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Salman Rahman
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Akwaugo Igwe
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Vijaya B. Kolachalama
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Computer Science and Faculty of Computing & Data Sciences, Boston University, Boston, MA, United States
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Rhoda Au
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| |
Collapse
|
13
|
Co-Design, Development, and Evaluation of a Health Monitoring Tool Using Smartwatch Data: A Proof-of-Concept Study. FUTURE INTERNET 2023. [DOI: 10.3390/fi15030111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023] Open
Abstract
Computational analysis and integration of smartwatch data with Electronic Medical Records (EMR) present potential uses in preventing, diagnosing, and managing chronic diseases. One of the key requirements for the successful clinical application of smartwatch data is understanding healthcare professional (HCP) perspectives on whether these devices can play a role in preventive care. Gaining insights from the vast amount of smartwatch data is a challenge for HCPs, thus tools are needed to support HCPs when integrating personalized health monitoring devices with EMR. This study aimed to develop and evaluate an application prototype, co-designed with HCPs and employing design science research methodology and diffusion of innovation frameworks to identify the potential for clinical integration. A machine learning algorithm was developed to detect possible health anomalies in smartwatch data, and these were presented visually to HCPs in a web-based platform. HCPs completed a usability questionnaire to evaluate the prototype, and over 60% of HCPs scored positively on usability. This preliminary study tested the proposed research to solve the practical challenges of HCP in interpreting smartwatch data before fully integrating smartwatches into the EMR. The findings provide design directions for future applications that use smartwatch data to improve clinical decision-making and reduce HCP workloads.
Collapse
|
14
|
Ratitch B, Rodriguez-Chavez IR, Dabral A, Fontanari A, Vega J, Onorati F, Vandendriessche B, Morton S, Damestani Y. Considerations for Analyzing and Interpreting Data from Biometric Monitoring Technologies in Clinical Trials. Digit Biomark 2022; 6:83-97. [PMID: 36466953 PMCID: PMC9716191 DOI: 10.1159/000525897] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 05/31/2022] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND The proliferation and increasing maturity of biometric monitoring technologies allow clinical investigators to measure the health status of trial participants in a more holistic manner, especially outside of traditional clinical settings. This includes capturing meaningful aspects of health in daily living and a more granular and objective manner compared to traditional tools in clinical settings. SUMMARY Within multidisciplinary teams, statisticians and data scientists are increasingly involved in clinical trials that incorporate digital clinical measures. They are called upon to provide input into trial planning, generation of evidence on the clinical validity of novel clinical measures, and evaluation of the adequacy of existing evidence. Analysis objectives related to demonstrating clinical validity of novel clinical measures differ from typical objectives related to demonstrating safety and efficacy of therapeutic interventions using established measures which statisticians are most familiar with. KEY MESSAGES This paper discusses key considerations for generating evidence for clinical validity through the lens of the type and intended use of a clinical measure. This paper also briefly discusses the regulatory pathways through which clinical validity evidence may be reviewed and highlights challenges that investigators may encounter while dealing with data from biometric monitoring technologies.
Collapse
Affiliation(s)
- Bohdana Ratitch
- Statistics and Data Insights, Bayer, Westmount, Québec, Canada
| | - Isaac R. Rodriguez-Chavez
- Strategy Center for Decentralized Clinical Trials and Digital Medicine, Drug Development Solutions, ICON plc, Blue Bell, Pennsylvania, USA
| | - Abhishek Dabral
- Global Development Operations, Amgen Inc., Thousand Oaks, California, USA
| | | | - Julio Vega
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Francesco Onorati
- Applied Data Science, Current Health, A Best Buy Health Company, Boston, Massachusetts, USA
| | - Benjamin Vandendriessche
- Byteflies, Antwerp, Belgium & Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Stuart Morton
- Emerging Digital Medicines, Eli Lilly & Co., Indianapolis, Indiana, USA
| | - Yasaman Damestani
- Digital Medicine, Karyopharm Therapeutics, Newton, Massachusetts, USA
| |
Collapse
|
15
|
Smart Consumer Wearables as Digital Diagnostic Tools: A Review. Diagnostics (Basel) 2022; 12:diagnostics12092110. [PMID: 36140511 PMCID: PMC9498278 DOI: 10.3390/diagnostics12092110] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 11/17/2022] Open
Abstract
The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.
Collapse
|