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Bazoukis G, Bollepalli SC, Chung CT, Li X, Tse G, Bartley BL, Batool-Anwar S, Quan SF, Armoundas AA. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med 2023; 19:1337-1363. [PMID: 36856067 PMCID: PMC10315608 DOI: 10.5664/jcsm.10532] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 02/21/2023] [Accepted: 02/21/2023] [Indexed: 03/02/2023]
Abstract
STUDY OBJECTIVES Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep-related breathing disorders. METHODS A systematic search in Medline, EMBASE, and Cochrane databases through January 2022 was performed. RESULTS Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry, and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy can be guided by ML models. CONCLUSIONS The adoption and implementation of ML in the field of sleep-related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose, and classify sleep apnea more accurately and efficiently. CITATION Bazoukis G, Bollepalli SC, Chung CT, et al. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med. 2023;19(7):1337-1363.
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Affiliation(s)
- George Bazoukis
- Department of Cardiology, Larnaca General Hospital, Larnaca, Cyprus
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
| | | | - Cheuk To Chung
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong
| | - Xinmu Li
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease, Department of Cardiology, Tianjin Institute of Cardiology, the Second Hospital of Tianjin Medical University, Tianjin, China
| | - Gary Tse
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong
- Kent and Medway Medical School, Canterbury, Kent, United Kingdom
| | - Bethany L. Bartley
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Salma Batool-Anwar
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Stuart F. Quan
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
- Asthma and Airway Disease Research Center, University of Arizona College of Medicine, Tucson, Arizona
| | - Antonis A. Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
- Broad Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts
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An Auto Adjustable Transimpedance Readout System for Wearable Healthcare Devices. ELECTRONICS 2022. [DOI: 10.3390/electronics11081181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The objective of this work was to design a versatile readout circuit for patch-type wearable devices consisting of a Transimpedance Amplifier (TIA). The TIA performs Current to Voltage (I–V) conversion, the most widely used technique for amperometry and impedance measurement for various types of electrochemical sensors. The proposed readout circuit employs a digitally controllable feedback resistor (Rf) technique in the TIA to improve accuracy, which can be utilized in a variety of electrochemical sensors within a current range of 0.1 µA–100 µA. It is designed to accommodate multiple sensors simultaneously to track multiple target analytes for high accuracy and versatile usage. The readout circuit consists of low power operational amplifier (op–amp) and digital circuit blocks, is designed and fabricated with Magna 0.18 µm Complementary Metal Oxide Semiconductor (CMOS) technology, which provides low power consumption and a high degree of integration. The design has a small size of 0.282 mm2 and low power consumption of 0.38 mW with a 3.3 V power supply, which are desirable factors in wearable device applications.
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Kulkarni K, Sevakula RK, Kassab MB, Nichols J, Roberts JD, Isselbacher EM, Armoundas AA. Ambulatory monitoring promises equitable personalized healthcare delivery in underrepresented patients. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:494-510. [PMID: 34604759 PMCID: PMC8482046 DOI: 10.1093/ehjdh/ztab047] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 03/28/2021] [Indexed: 01/30/2023]
Abstract
The pandemic has brought to everybody's attention the apparent need of remote monitoring, highlighting hitherto unseen challenges in healthcare. Today, mobile monitoring and real-time data collection, processing and decision-making, can drastically improve the cardiorespiratory-haemodynamic health diagnosis and care, not only in the rural communities, but urban ones with limited healthcare access as well. Disparities in socioeconomic status and geographic variances resulting in regional inequity in access to healthcare delivery, and significant differences in mortality rates between rural and urban communities have been a growing concern. Evolution of wireless devices and smartphones has initiated a new era in medicine. Mobile health technologies have a promising role in equitable delivery of personalized medicine and are becoming essential components in the delivery of healthcare to patients with limited access to in-hospital services. Yet, the utility of portable health monitoring devices has been suboptimal due to the lack of user-friendly and computationally efficient physiological data collection and analysis platforms. We present a comprehensive review of the current cardiac, pulmonary, and haemodynamic telemonitoring technologies. We also propose a novel low-cost smartphone-based system capable of providing complete cardiorespiratory assessment using a single platform for arrhythmia prediction along with detection of underlying ischaemia and sleep apnoea; we believe this system holds significant potential in aiding the diagnosis and treatment of cardiorespiratory diseases, particularly in underserved populations.
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Affiliation(s)
- Kanchan Kulkarni
- Cardiovascular Research Center, Massachusetts General Hospital, 149 13th Street, Boston, MA 02129, USA
| | - Rahul Kumar Sevakula
- Cardiovascular Research Center, Massachusetts General Hospital, 149 13th Street, Boston, MA 02129, USA
| | - Mohamad B Kassab
- Cardiovascular Research Center, Massachusetts General Hospital, 149 13th Street, Boston, MA 02129, USA
| | - John Nichols
- Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Jesse D. Roberts
- Cardiovascular Research Center, Massachusetts General Hospital, 149 13th Street, Boston, MA 02129, USA,Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Eric M Isselbacher
- Healthcare Transformation Lab, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Antonis A Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital, 149 13th Street, Boston, MA 02129, USA,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA,Corresponding author. Tel: +617-726-0930,
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Kulkarni K, Walton RD, Armoundas AA, Tolkacheva EG. Clinical Potential of Beat-to-Beat Diastolic Interval Control in Preventing Cardiac Arrhythmias. J Am Heart Assoc 2021; 10:e020750. [PMID: 34027678 PMCID: PMC8483541 DOI: 10.1161/jaha.121.020750] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Life‐threatening ventricular arrhythmias and sudden cardiac death are often preceded by cardiac alternans, a beat‐to‐beat oscillation in the T‐wave morphology or duration. However, given the spatiotemporal and structural complexity of the human heart, designing algorithms to effectively suppress alternans and prevent fatal rhythms is challenging. Recently, an antiarrhythmic constant diastolic interval pacing protocol was proposed and shown to be effective in suppressing alternans in 0‐, 1‐, and 2‐dimensional in silico studies as well as in ex vivo whole heart experiments. Herein, we provide a systematic review of the electrophysiological conditions and mechanisms that enable constant diastolic interval pacing to be an effective antiarrhythmic pacing strategy. We also demonstrate a successful translation of the constant diastolic interval pacing protocol into an ECG‐based real‐time control system capable of modulating beat‐to‐beat cardiac electrical activity and preventing alternans. Furthermore, we present evidence of the clinical utility of real‐time alternans suppression in reducing arrhythmia susceptibility in vivo. We provide a comprehensive overview of this promising pacing technique, which can potentially be translated into a clinically viable device that could radically improve the quality of life of patients experiencing abnormal cardiac rhythms.
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Affiliation(s)
- Kanchan Kulkarni
- IHU-LIRYC, Electrophysiology and Heart Modeling InstituteFondation Bordeaux Université Pessac, Bordeaux France.,Centre de Recherche Cardio-Thoracique de Bordeaux University of Bordeaux France.,Centre de Recherche Cardio-Thoracique de Bordeaux INSERM Bordeaux France
| | - Richard D Walton
- IHU-LIRYC, Electrophysiology and Heart Modeling InstituteFondation Bordeaux Université Pessac, Bordeaux France.,Centre de Recherche Cardio-Thoracique de Bordeaux University of Bordeaux France.,Centre de Recherche Cardio-Thoracique de Bordeaux INSERM Bordeaux France
| | - Antonis A Armoundas
- Cardiovascular Research Center Massachusetts General Hospital Boston MA.,Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA
| | - Elena G Tolkacheva
- Department of Biomedical Engineering University of Minnesota-Twin Cities Minneapolis MN
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Kulkarni K, Awasthi N, Roberts JD, Armoundas AA. Utility of a Smartphone-Based System (cvrPhone) in Estimating Minute Ventilation from Electrocardiographic Signals. Telemed J E Health 2021; 27:1433-1439. [PMID: 33729001 DOI: 10.1089/tmj.2020.0507] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: We investigated the ability of a novel stand-alone, smartphone-based system, the cvrPhone, in estimating the minute ventilation (MV) from body surface electrocardiographic (ECG) signals. Methods: Twelve lead ECG signals were collected from anesthetized and mechanically ventilated swine (n = 9) using standard surface electrodes and the cvrPhone. The tidal volume delivered to the animals was varied between 0, 250, 500, and 750 mL at respiration rates of 6 and 14 breaths/min. MV estimates were determined by the cvrPhone and were compared with the delivered ones. Results: The median relative estimation errors were 17%, -4%, 35%, -3%, -9%, and 1%, for true MVs of 1,500, 3,000, 3,500, 4,500, 7,000, and 10,500 breaths*mL/min, respectively. The MV estimates at each of the settings were significantly different from each other (p < 0.05). Conclusions: We have demonstrated that accurate MV estimations can be derived from standard body surface ECG signals, using a smartphone.
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Affiliation(s)
- Kanchan Kulkarni
- Cardiovascular Research Center, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Navchetan Awasthi
- Cardiovascular Research Center, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jesse D Roberts
- Cardiovascular Research Center, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Antonis A Armoundas
- Cardiovascular Research Center, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology Cambridge, Massachusetts, USA
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Wearable Devices for Ambulatory Cardiac Monitoring: JACC State-of-the-Art Review. J Am Coll Cardiol 2020; 75:1582-1592. [PMID: 32241375 DOI: 10.1016/j.jacc.2020.01.046] [Citation(s) in RCA: 117] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 01/20/2020] [Accepted: 01/27/2020] [Indexed: 12/14/2022]
Abstract
Ambulatory monitoring devices are enabling a new paradigm of health care by collecting and analyzing long-term data for reliable diagnostics. These devices are becoming increasingly popular for continuous monitoring of cardiac diseases. Recent advancements have enabled solutions that are both affordable and reliable, allowing monitoring of vulnerable populations from the comfort of their homes. They provide early detection of important physiological events, leading to timely alerts for seeking medical attention. In this review, the authors aim to summarize the recent developments in the area of ambulatory and remote monitoring solutions for cardiac diagnostics. The authors cover solutions based on wearable devices, smartphones, and other ambulatory sensors. The authors also present an overview of the limitations of current technologies, their effectiveness, and their adoption in the general population, and discuss some of the recently proposed methods to overcome these challenges. Lastly, we discuss the possibilities opened by this new paradigm, for the future of health care and personalized medicine.
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Design Implementation and Evaluation of a Mobile Continuous Blood Oxygen Saturation Monitoring System. SENSORS 2020; 20:s20226581. [PMID: 33217945 PMCID: PMC7698638 DOI: 10.3390/s20226581] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 11/13/2020] [Accepted: 11/13/2020] [Indexed: 01/31/2023]
Abstract
Objective: In this study, we built a mobile continuous Blood Oxygen Saturation (SpO2) monitor, and for the first time, explored key design principles towards daily applications. Methods: We firstly built a customized wearable computer that can sense two-channel photoplethysmogram (PPG) signals, and transmit the signals wirelessly to smartphone. Afterwards, we explored many SpO2 model building principles, focusing on linear/nonlinear models, different PPG parameter calculation methods, and different finger types. Moreover, we further compared PPG sensor placement principles by comparing different hand configurations and different finger configurations. Finally, a dataset collected from eleven human subjects was used to evaluate the mobile health monitor and explore all of the above design principles. Results: The experimental results show that the root mean square error of the SpO2 estimation is only 1.8, indicating the effectiveness of the system. Conclusion: These results indicate the effectiveness of the customized mobile SpO2 monitor and the selected design principles. Significance: This research is expected to facilitate the continuous SpO2 monitoring of patients with clinical indications.
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