Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps lowdimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.

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“…Biomedical ECG, EMG and MCG signal processing [209,210,211,212,213,214]. Medical images segmentation and classification [215].…”

Section: Research Fields Applicationsmentioning

confidence: 99%

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Zhang

Vargas

2023

IEEE Access

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“…Ideally, a stratified group cross-validation is used, but none of the studies used this. In addition to validation strategies, it is important to use replication data, that is, completely independently acquired data, which was only done in 11 [17,18,21,24,25,31,33,35,36,40,70] studies.…”

Section: Validation (Levels 7 and 8)mentioning

confidence: 99%

Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View

et al. 2022

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Background Wearable technology has the potential to improve cardiovascular health monitoring by using machine learning. Such technology enables remote health monitoring and allows for the diagnosis and prevention of cardiovascular diseases. In addition to the detection of cardiovascular disease, it can exclude this diagnosis in symptomatic patients, thereby preventing unnecessary hospital visits. In addition, early warning systems can aid cardiologists in timely treatment and prevention. Objective This study aims to systematically assess the literature on detecting and predicting outcomes of patients with cardiovascular diseases by using machine learning with data obtained from wearables to gain insights into the current state, challenges, and limitations of this technology. Methods We searched PubMed, Scopus, and IEEE Xplore on September 26, 2020, with no restrictions on the publication date and by using keywords such as “wearables,” “machine learning,” and “cardiovascular disease.” Methodologies were categorized and analyzed according to machine learning–based technology readiness levels (TRLs), which score studies on their potential to be deployed in an operational setting from 1 to 9 (most ready). Results After the removal of duplicates, application of exclusion criteria, and full-text screening, 55 eligible studies were included in the analysis, covering a variety of cardiovascular diseases. We assessed the quality of the included studies and found that none of the studies were integrated into a health care system (TRL<6), prospective phase 2 and phase 3 trials were absent (TRL<7 and 8), and group cross-validation was rarely used. These issues limited these studies’ ability to demonstrate the effectiveness of their methodologies. Furthermore, there seemed to be no agreement on the sample size needed to train these studies’ models, the size of the observation window used to make predictions, how long participants should be observed, and the type of machine learning model that is suitable for predicting cardiovascular outcomes. Conclusions Although current studies show the potential of wearables to monitor cardiovascular events, their deployment as a diagnostic or prognostic cardiovascular clinical tool is hampered by the lack of a realistic data set and proper systematic and prospective evaluation.

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“…The special issue consists of 10 contributions in the area of wearable wireless devices: In the paper, 'Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification' [1] a Reservoir Computing (RC) based Echo State Networks (ESNs) for ventricular heartbeat classification based on a single Electrocardiogram (ECG) lead has been proposed. The proposed method was especially designed for Medical Internet of Things (MIoT) devices, for instance wearable wireless devices for ECG monitoring or ventricular heart beat detection systems and so on.…”

Section: Contributionsmentioning

confidence: 99%

Wearable Wireless Devices

2019

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Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification

Cited by 20 publications

References 51 publications

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View

Wearable Wireless Devices

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