Detection of Cardiac Arrhythmias From Varied Length Multichannel Electrocardiogram Recordings Using Deep Convolutional Neural Networks

Sallem, Marwen; Ghrissi, Amina; Saadaoui, Adnen; Zarzoso, Vicente

doi:10.22489/cinc.2020.339

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…We evaluate the binary AFib classification using 4-second ECG windows. We apply Shorttime Fourier Transform (STFT) on the generated ECG and use it as input to a standard VGG-13 (Sallem et al 2020).…”

Section: Discussionmentioning

confidence: 99%

Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation

Shome,

Sarkar,

Etemad

2024

AAAI

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The high prevalence of cardiovascular diseases (CVDs) calls for accessible and cost-effective continuous cardiac monitoring tools. Despite Electrocardiography (ECG) being the gold standard, continuous monitoring remains a challenge, leading to the exploration of Photoplethysmography (PPG), a promising but more basic alternative available in consumer wearables. This notion has recently spurred interest in translating PPG to ECG signals. In this work, we introduce Region-Disentangled Diffusion Model (RDDM), a novel diffusion model designed to capture the complex temporal dynamics of ECG. Traditional Diffusion models like Denoising Diffusion Probabilistic Models (DDPM) face challenges in capturing such nuances due to the indiscriminate noise addition process across the entire signal. Our proposed RDDM overcomes such limitations by incorporating a novel forward process that selectively adds noise to specific regions of interest (ROI) such as QRS complex in ECG signals, and a reverse process that disentangles the denoising of ROI and non-ROI regions. Quantitative experiments demonstrate that RDDM can generate high-fidelity ECG from PPG in as few as 10 diffusion steps, making it highly effective and computationally efficient. Additionally, to rigorously validate the usefulness of the generated ECG signals, we introduce CardioBench, a comprehensive evaluation benchmark for a variety of cardiac-related tasks including heart rate and blood pressure estimation, stress classification, and the detection of atrial fibrillation and diabetes. Our thorough experiments show that RDDM achieves state-of-the-art performance on CardioBench. To the best of our knowledge, RDDM is the first diffusion model for cross-modal signal-to-signal translation in the bio-signal domain.

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Section: Discussionmentioning

confidence: 99%

Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation

Shome,

Sarkar,

Etemad

2024

AAAI

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“…The respiratory effort classifier (lower half of Fig. 4) is a CNN based on architectures that have proven successful for the analysis of 1-dimensional physiological signals such as ECG [18][19][20]. Similar to its acoustic counterpart, it takes as input the raw effort signal for a 30-s segment, and outputs the probability of the segment containing apneahypopnea events.…”

Section: Late Decision Fusionmentioning

confidence: 99%

Obstructive sleep apnea screening with breathing sounds and respiratory effort: a multimodal deep learning approach

Romero¹,

Brown²,

Johnson³

2023

Interspeech 2023

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Obstructive sleep apnea (OSA) is a chronic and prevalent condition with well-established comorbidities. Due to limited diagnostic resources and high cost, a significant OSA population lives undiagnosed, and accurate and low-cost methods to screen for OSA are needed. We propose a novel screening method based on breathing sounds recorded with a smartphone and respiratory effort. Whole night recordings are divided into 30-s segments, each of which is classified for the presence or absence of OSA events by a multimodal deep neural network. Data fusion techniques were investigated and evaluated based on the apnea-hypopnea index estimated from whole night recordings. Real-world recordings made during home sleep apnea testing from 103 participants were used to develop and evaluate the proposed system. The late fusion system achieved the best sensitivity and specificity when screening for severe OSA, at 0.93 and 0.92, respectively. This offers the prospect of inexpensive OSA screening at home.

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“…However, in other arrhythmia categories, the 1D-TCNFN can effectively detect different arrhythmia conditions, with an accuracy ranging from 80 to 100%. To demonstrate the superiority of the proposed method, seven deep learning models with different architectures, namely, 1D-LeNet, (23) 1D-AlexNet, (24) 1D-VGG16, (25) 1D-GoogLeNet, (26) 1D-ResNet18, (27) 1D-ResNet50, (28) and 1D-CNFN (without Taguchi optimization), were used to compare the detection performance characteristics (Table 7). When the global average pooling or global max pooling fusion method was employed in the proposed 1D-CNFN, an accuracy of 88.95 or 91.45%, respectively, was achieved.…”

Section: Model Evaluation For Arrhythmia Detectionmentioning

confidence: 99%

Arrhythmia Detection Using a Taguchi-based Convolutional Neuro-fuzzy Network

Li¹,

Jhang²,

Lin³

et al. 2022

Sensors and Materials

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With improvements in the quality of life, people have paid increased attention to their health. According to the American Heart Association, cardiovascular disease was one of the leading causes of death globally as of 2016. Medical experts estimate that the worldwide annual number of people dying from cardiovascular disease will reach 23.6 million by 2030. Detecting heart arrhythmias effectively and quickly is critical for preventing cardiovascular disease. In this paper, a one-dimensional Taguchi-based convolutional neuro-fuzzy network (1D-TCNFN) for detecting arrhythmia in electrocardiograms (ECGs) is proposed. The proposed 1D-TCNFN adopts neuro-fuzzy instead of conventionally connected layers to reduce the number of learned parameters in the network. Four feature fusion methods, namely, global average pooling, global max pooling, channel average pooling, and channel max pooling, are employed in the 1D-TCNFN. For an increased detection accuracy, the Taguchi method was used to optimize the network architecture of the proposed 1D-TCNFN. In the experiments, the open Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database was adopted to verify the performance of the proposed method for detecting 17 different arrhythmia signals.The proposed 1D-TCNFN exhibited a detection accuracy of 93.95% for the MIT-BIH Arrhythmia Database.

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Detection of Cardiac Arrhythmias From Varied Length Multichannel Electrocardiogram Recordings Using Deep Convolutional Neural Networks

Cited by 3 publications

References 10 publications

Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation

Region-Disentangled Diffusion Model for High-Fidelity PPG-to-ECG Translation

Obstructive sleep apnea screening with breathing sounds and respiratory effort: a multimodal deep learning approach

Arrhythmia Detection Using a Taguchi-based Convolutional Neuro-fuzzy Network

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