Robust ECG R-peak detection using LSTM

Laitala, Juho; Jiang, Mingzhe; Syrjälä, Elise; Naeini, Emad Kasaeyan; Airola, Antti; Rahmani, Amir M.; Dutt, Nikil; Liljeberg, Pasi

doi:10.1145/3341105.3373945

Cited by 47 publications

(39 citation statements)

References 25 publications

(31 reference statements)

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“…The model's output is further verified by another (verification) model that detects the false alarms. Unlike other approaches [35][36], all beats including arrhythmia beats were used for training and detection as the detection of arrhythmic beats is the most challenging problem.…”

Section: Methodsmentioning

confidence: 99%

Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network

Zahid

Kıranyaz

İnce

et al. 2022

IEEE Trans. Biomed. Eng.

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Noise and low quality of ECG signals acquired from Holter or wearable devices deteriorate the accuracy and robustness of R-peak detection algorithms. This paper presents a generic and robust system for R-peak detection in Holter ECG signals. While many proposed algorithms have successfully addressed the problem of ECG Rpeak detection, there is still a notable gap in the performance of these detectors on such low-quality ECG records. Methods: In this study, a novel implementation of the 1D Convolutional Neural Network (CNN) is used integrated with a verification model to reduce the number of false alarms. This CNN architecture consists of an encoder block and a corresponding decoder block followed by a sample-wise classification layer to construct the 1D segmentation map of R-peaks from the input ECG signal. Once the proposed model has been trained, it can solely be used to detect R-peaks possibly in a single channel ECG data stream quickly and accurately, or alternatively, such a solution can be conveniently employed for real-time monitoring on a lightweight portable device. Results: The model is tested on two open-access ECG databases: The China Physiological Signal Challenge (2020) database (CPSC-DB) with more than one million beats, and the commonly used MIT-BIH Arrhythmia Database (MIT-DB).Experimental results demonstrate that the proposed systematic approach achieves 99.30% F1-score, 99.69% recall, and 98.91% precision in CPSC-DB, which is the best R-peak detection performance ever achieved. Results also demonstrate similar or better performance than most competing algorithms on MIT-DB with 99.83% F1-score, 99.85% recall, and 99.82% precision. Significance: Compared to all competing methods, the proposed approach can reduce the false-positives and falsenegatives in Holter ECG signals by more than 54% and 82%, respectively. Conclusion: Finally, the simple and invariant nature of the parameters leads to a highly generic system and therefore applicable to any ECG dataset.

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Section: Methodsmentioning

confidence: 99%

Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network

Zahid

Kıranyaz

İnce

et al. 2022

IEEE Trans. Biomed. Eng.

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“…To find the peaks, the signals were first sampled down to 250 Hz. A bidirectional long short-term memory network was used to obtain the probabilities and locations of peaks [ 27 ]. A window size of 1000 samples and stride of 100 samples was used to generate these predictions.…”

Section: Methodsmentioning

confidence: 99%

Pain Recognition With Electrocardiographic Features in Postoperative Patients: Method Validation Study

Naeini¹,

Subramanian²,

Calderon³

et al. 2021

J Med Internet Res

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Background There is a strong demand for an accurate and objective means of assessing acute pain among hospitalized patients to help clinicians provide pain medications at a proper dosage and in a timely manner. Heart rate variability (HRV) comprises changes in the time intervals between consecutive heartbeats, which can be measured through acquisition and interpretation of electrocardiography (ECG) captured from bedside monitors or wearable devices. As increased sympathetic activity affects the HRV, an index of autonomic regulation of heart rate, ultra–short-term HRV analysis can provide a reliable source of information for acute pain monitoring. In this study, widely used HRV time and frequency domain measurements are used in acute pain assessments among postoperative patients. The existing approaches have only focused on stimulated pain in healthy subjects, whereas, to the best of our knowledge, there is no work in the literature building models using real pain data and on postoperative patients. Objective The objective of our study was to develop and evaluate an automatic and adaptable pain assessment algorithm based on ECG features for assessing acute pain in postoperative patients likely experiencing mild to moderate pain. Methods The study used a prospective observational design. The sample consisted of 25 patient participants aged 18 to 65 years. In part 1 of the study, a transcutaneous electrical nerve stimulation unit was employed to obtain baseline discomfort thresholds for the patients. In part 2, a multichannel biosignal acquisition device was used as patients were engaging in non-noxious activities. At all times, pain intensity was measured using patient self-reports based on the Numerical Rating Scale. A weak supervision framework was inherited for rapid training data creation. The collected labels were then transformed from 11 intensity levels to 5 intensity levels. Prediction models were developed using 5 different machine learning methods. Mean prediction accuracy was calculated using leave-one-out cross-validation. We compared the performance of these models with the results from a previously published research study. Results Five different machine learning algorithms were applied to perform a binary classification of baseline (BL) versus 4 distinct pain levels (PL1 through PL4). The highest validation accuracy using 3 time domain HRV features from a BioVid research paper for baseline versus any other pain level was achieved by support vector machine (SVM) with 62.72% (BL vs PL4) to 84.14% (BL vs PL2). Similar results were achieved for the top 8 features based on the Gini index using the SVM method, with an accuracy ranging from 63.86% (BL vs PL4) to 84.79% (BL vs PL2). Conclusions We propose a novel pain assessment method for postoperative patients using ECG signal. Weak supervision applied for labeling and feature extraction improves the robustness of the approach. Our results show the viability of using a machine learning algorithm to accurately and objectively assess acute pain among hospitalized patients. International Registered Report Identifier (IRRID) RR2-10.2196/17783

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“…The most well-known type of RNNs are LSTMs which are designed to mine patterns in data sequences using their short-term memory of distant events stored in their memory cells. LSTMs have been widely used for processing biomedical signals such as ECGs [33], [36]. Although there are many other variants of DNN architectures, we will focus on these most commonly used types.…”

Section: B Deep Artificial Neural Networkmentioning

confidence: 99%

“…Table II lists three general purpose AI accelerator chips, which have been deployed for low-cost and easy-to-access skin cancer detection using MobileNet V1 CNN [25], on edge health monitoring for fall detection using LSTMs [74], chest X-ray analysis using ResNet CNN [76], long term bowel sound monitoring and segmentation using a CNN [77], cardiovascular arrhythmia detection from ECG using an LSTM [33], or heart rate variability analysis from ECG signals through a bidirectional LSTM [36], just to name a few. These general-purpose chips have the potential to be used for other biomedical edge-based applications such as robust long-term decoding in intracortical BMIs using MLP and ELM networks in a sparse ensemble machine learning platform [75].…”

Section: ) Edge-ai Dnn Accelerators Suitable For Biomedical Applicationsmentioning

confidence: 99%

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

Azghadi

Lammie

Eshraghian

et al. 2020

IEEE Trans. Biomed. Circuits Syst.

137

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The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing how various technologies including emerging memristive devices, Field Programmable Gate Arrays (FPGAs), and Complementary Metal Oxide Semiconductor (CMOS) can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare. Furthermore, we explore how spiking neuromorphic processors can complement their DL counterparts for processing biomedical signals. The tutorial is augmented with case studies of the vast literature on neural network and neuromorphic hardware as applied to the healthcare domain. We benchmark various hardware platforms by performing a sensor fusion signal processing task combining electromyography (EMG) signals with computer vision. Comparisons are made between dedicated neuromorphic processors and embedded AI accelerators in terms of inference latency and energy. Finally, we provide our analysis of the field and share a perspective on the advantages, disadvantages, challenges, and opportunities that various accelerators and neuromorphic processors introduce to healthcare and biomedical domains.

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Robust ECG R-peak detection using LSTM

Cited by 47 publications

References 25 publications

Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network

Robust R-Peak Detection in Low-Quality Holter ECGs Using 1D Convolutional Neural Network

Pain Recognition With Electrocardiographic Features in Postoperative Patients: Method Validation Study

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

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