1
|
Mandala S, Rizal A, Adiwijaya, Nurmaini S, Suci Amini S, Almayda Sudarisman G, Wen Hau Y, Hanan Abdullah A. An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG). PLoS One 2024; 19:e0297551. [PMID: 38593145 PMCID: PMC11003640 DOI: 10.1371/journal.pone.0297551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 01/09/2024] [Indexed: 04/11/2024] Open
Abstract
Arrhythmia is a life-threatening cardiac condition characterized by irregular heart rhythm. Early and accurate detection is crucial for effective treatment. However, single-lead electrocardiogram (ECG) methods have limited sensitivity and specificity. This study propose an improved ensemble learning approach for arrhythmia detection using multi-lead ECG data. Proposed method, based on a boosting algorithm, namely Fine Tuned Boosting (FTBO) model detects multiple arrhythmia classes. For the feature extraction, introduce a new technique that utilizes a sliding window with a window size of 5 R-peaks. This study compared it with other models, including bagging and stacking, and assessed the impact of parameter tuning. Rigorous experiments on the MIT-BIH arrhythmia database focused on Premature Ventricular Contraction (PVC), Atrial Premature Contraction (PAC), and Atrial Fibrillation (AF) have been performed. The results showed that the proposed method achieved high sensitivity, specificity, and accuracy for all three classes of arrhythmia. It accurately detected Atrial Fibrillation (AF) with 100% sensitivity and specificity. For Premature Ventricular Contraction (PVC) detection, it achieved 99% sensitivity and specificity in both leads. Similarly, for Atrial Premature Contraction (PAC) detection, proposed method achieved almost 96% sensitivity and specificity in both leads. The proposed method shows great potential for early arrhythmia detection using multi-lead ECG data.
Collapse
Affiliation(s)
- Satria Mandala
- Human Centric (HUMIC) Engineering, Telkom University, Bandung, Indonesia
- School of Computing, Telkom University, Bandung, Indonesia
| | - Ardian Rizal
- Department of Cardiology and Vascular Medicine, Faculty of Medicine, Universitas Brawijaya, Malang, East Java, Indonesia
| | - Adiwijaya
- Human Centric (HUMIC) Engineering, Telkom University, Bandung, Indonesia
- School of Computing, Telkom University, Bandung, Indonesia
| | - Siti Nurmaini
- Intelligent System Research Group, Universitas Sriwijaya, Palembang, South Sumatra, Indonesia
| | | | | | - Yuan Wen Hau
- IJN-UTM Cardiovascular Engineering Centre, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia
| | - Abdul Hanan Abdullah
- Human Centric (HUMIC) Engineering, Telkom University, Bandung, Indonesia
- Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Johor, Malaysia
| |
Collapse
|
2
|
Kolk MZH, Deb B, Ruipérez-Campillo S, Bhatia NK, Clopton P, Wilde AAM, Narayan SM, Knops RE, Tjong FVY. Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies. EBioMedicine 2023; 89:104462. [PMID: 36773349 PMCID: PMC9945642 DOI: 10.1016/j.ebiom.2023.104462] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 01/19/2023] [Accepted: 01/19/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND Ventricular arrhythmia (VA) precipitating sudden cardiac arrest (SCD) is among the most frequent causes of death and pose a high burden on public health systems worldwide. The increasing availability of electrophysiological signals collected through conventional methods (e.g. electrocardiography (ECG)) and digital health technologies (e.g. wearable devices) in combination with novel predictive analytics using machine learning (ML) and deep learning (DL) hold potential for personalised predictions of arrhythmic events. METHODS This systematic review and exploratory meta-analysis assesses the state-of-the-art of ML/DL models of electrophysiological signals for personalised prediction of malignant VA or SCD, and studies potential causes of bias (PROSPERO, reference: CRD42021283464). Five electronic databases were searched to identify eligible studies. Pooled estimates of the diagnostic odds ratio (DOR) and summary area under the curve (AUROC) were calculated. Meta-analyses were performed separately for studies using publicly available, ad-hoc datasets, versus targeted clinical data acquisition. Studies were scored on risk of bias by the PROBAST tool. FINDINGS 2194 studies were identified of which 46 were included in the systematic review and 32 in the meta-analysis. Pooling of individual models demonstrated a summary AUROC of 0.856 (95% CI 0.755-0.909) for short-term (time-to-event up to 72 h) prediction and AUROC of 0.876 (95% CI 0.642-0.980) for long-term prediction (time-to-event up to years). While models developed on ad-hoc sets had higher pooled performance (AUROC 0.919, 95% CI 0.867-0.952), they had a high risk of bias related to the re-use and overlap of small ad-hoc datasets, choices of ML tool and a lack of external model validation. INTERPRETATION ML and DL models appear to accurately predict malignant VA and SCD. However, wide heterogeneity between studies, in part due to small ad-hoc datasets and choice of ML model, may reduce the ability to generalise and should be addressed in future studies. FUNDING This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).
Collapse
Affiliation(s)
- Maarten Z H Kolk
- Amsterdam UMC Location University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart failure & arrhythmias, Amsterdam, The Netherlands
| | - Brototo Deb
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | | | - Neil K Bhatia
- Department of Cardiology, Emory University, Atlanta, GA, USA
| | - Paul Clopton
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Arthur A M Wilde
- Amsterdam UMC Location University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart failure & arrhythmias, Amsterdam, The Netherlands
| | - Sanjiv M Narayan
- Department of Medicine and Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Reinoud E Knops
- Amsterdam UMC Location University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart failure & arrhythmias, Amsterdam, The Netherlands
| | - Fleur V Y Tjong
- Amsterdam UMC Location University of Amsterdam, Heart Center, Department of Clinical and Experimental Cardiology, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Heart failure & arrhythmias, Amsterdam, The Netherlands.
| |
Collapse
|
3
|
Gungor CB, Mercier PP, Toreyin H. A 1.2nW Analog Electrocardiogram Processor Achieving a 99.63% QRS Complex Detection Sensitivity. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:617-628. [PMID: 34185648 DOI: 10.1109/tbcas.2021.3092729] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
An energy-efficient electrocardiogram (ECG) processor for real-time QRS detection is presented. The proposed algorithm is based on the Pan-Tompkins algorithm and it is implemented in the analog domain leveraging ultra-low power analog electronics biased in subthreshold. Operational transconductance amplifiers with ∼100 mV linear range are used in almost all of the processing blocks, while squaring is performed on current signals. Additionally, instead of adaptive thresholding, a fixed-level thresholding is performed, thereby eliminating the need for additional blocks such as memory and threshold update. The processor is designed in 65 nm TSMC CMOS technology and has a footprint of 0.078 mm2. When supplied by a 1 V supply, the processor consumes 1.2 nW. Using the recordings in the MIT-BIH database, the processor achieves an average QRS detection sensitivity of 99.63% and positive predictivity of 99.47%.
Collapse
|
4
|
Advances of ECG Sensors from Hardware, Software and Format Interoperability Perspectives. ELECTRONICS 2021. [DOI: 10.3390/electronics10020105] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
It is well-known that cardiovascular disease is one of the major causes of death worldwide nowadays. Electrocardiogram (ECG) sensor is one of the tools commonly used by cardiologists to diagnose and detect signs of heart disease with their patients. Since fast, prompt and accurate interpretation and decision is important in saving the life of patients from sudden heart attack or cardiac arrest, many innovations have been made to ECG sensors. However, the use of traditional ECG sensors is still prevalent in the clinical settings of many medical institutions. This article provides a comprehensive survey on ECG sensors from hardware, software and data format interoperability perspectives. The hardware perspective outlines a general hardware architecture of an ECG sensor along with the description of its hardware components. The software perspective describes various techniques (denoising, machine learning, deep learning, and privacy preservation) and other computer paradigms used in the software development and deployment for ECG sensors. Finally, the format interoperability perspective offers a detailed taxonomy of current ECG formats and the relationship among these formats. The intention is to help researchers towards the development of modern ECG sensors that are suitable and approved for adoption in real clinical settings.
Collapse
|