Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors

Zhong, Jun; Hai, Dong; Cheng, Jiaxin; Jiao, Changzhe; Gou, Shuiping; Liu, Yongfeng; Zhou, Hai; Zhu, Wenliang

doi:10.3390/s21217163

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…Manogaran et al proposed a modeling method combining Hidden Markov Model (HMM) and DBSCAN with GMM [6,7]. Zhong Jun et al proposed a hybrid algorithm of convolutional auto-encoding and Gaussian mixture, which was applied to the feature extraction of ECG signals, and saved a lot of time and effort of manual labeling [8]. Shi Yongge et al proposed a hybrid algorithm of Kmeans and Extreme Gradient Boosting (XGBoost) to mine designated telecom customers with special behaviors from the vast voice communication records of telecom companies [9].…”

Section: Related Workmentioning

confidence: 99%

Analysis of influencing factors on excellent teachers' professional growth based on DB-Kmeans method

Gao

Ding

Han

et al. 2022

EURASIP J. Adv. Signal Process.

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The Kmeans clustering algorithm is widely used for the advantages of simplicity and efficient operation. However, the lack of clustering centers in the algorithm usually causes incorrect category of some discrete points. Therefore, in order to obtain more accurate clustering results when studying the factors affecting the professional growth of outstanding teachers, this paper proposes an improved algorithm of Kmeans combined with DBSCAN. Observing the clustering results of the influencing factors and calculating the evaluation standard values of the clustering results, it is found that the optimized DB-Kmeans algorithm has obvious improvements in the accuracy of the clustering results, and the clustering effect of the algorithm on edge points is more advantageous than the original algorithms according to the scatter diagram.

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Section: Related Workmentioning

confidence: 99%

Analysis of influencing factors on excellent teachers' professional growth based on DB-Kmeans method

Gao

Ding

Han

et al. 2022

EURASIP J. Adv. Signal Process.

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Heart Rate Anomaly Detection Using Contractive Autoencoder for Smartwatch-Based Health Monitoring

Sivan,

Hu,

Aslam

et al. 2023

2023 15th International Conference on Software, Knowledge, Information Management and Applications (SKIMA)

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Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram

Terzi,

Arikan

2023

Biomedical Engineering / Biomedizinische Technik

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Objectives Coronary artery diseases (CADs) are the leading cause of death worldwide and early diagnosis is crucial for timely treatment. To address this, our study presents a novel automated Artificial Intelligence (AI)-based Hybrid Anomaly Detection (AIHAD) technique that combines various signal processing, feature extraction, supervised, and unsupervised machine learning methods. By jointly and simultaneously analyzing 12-lead cardiac sympathetic nerve activity (CSNA) and electrocardiogram (ECG) data, the automated AIHAD technique performs fast, early, and accurate diagnosis of CADs. Methods In order to develop and evaluate the proposed automated AIHAD technique, we utilized the fully labeled STAFF III and PTBD databases, which contain the 12-lead wideband raw recordings non-invasively acquired from 260 subjects. Using these wideband raw recordings, we developed a signal processing technique that simultaneously detects the 12-lead CSNA and ECG signals of all subjects. Using the pre-processed 12-lead CSNA and ECG signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of CADs. Using the extracted discriminative features, we developed a supervised classification technique based on Artificial Neural Networks (ANNs) that simultaneously detects anomalies in the 12-lead CSNA and ECG data. Furthermore, we developed an unsupervised clustering technique based on Gaussian mixture models (GMMs) and Neyman-Pearson criterion, which robustly detects outliers corresponding to CADs. Results Using the automated AIHAD technique, we have, for the first time, demonstrated a significant association between the increase in CSNA signals and anomalies in ECG signals during CADs. The AIHAD technique achieved highly reliable detection of CADs with a sensitivity of 98.48 %, specificity of 97.73 %, accuracy of 98.11 %, positive predictive value of 97.74 %, negative predictive value of 98.47 %, and F1-score of 98.11 %. Hence, the automated AIHAD technique demonstrates superior performance compared to the gold standard diagnostic test ECG in the diagnosis of CADs. Additionally, it outperforms other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it significantly increases the detection performance of CADs by taking advantage of the diversity in different data types and leveraging their strengths. Furthermore, its performance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or classify CADs. Additionally, it has a very low implementation time, which is highly desirable for real-time detection of CADs. Conclusions The proposed automated AIHAD technique may serve as an efficient decision-support system to increase physicians’ success in fast, early, and accurate diagnosis of CADs. It may be highly beneficial and valuable, particularly for asymptomatic patients, for whom the diagnostic information provided by ECG alone is not sufficient to reliably diagnose the disease. Hence, it may significantly improve patient outcomes by enabling timely treatments and considerably reducing the mortality of cardiovascular diseases (CVDs).

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Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors

Cited by 4 publications

References 36 publications

Analysis of influencing factors on excellent teachers' professional growth based on DB-Kmeans method

Analysis of influencing factors on excellent teachers' professional growth based on DB-Kmeans method

Heart Rate Anomaly Detection Using Contractive Autoencoder for Smartwatch-Based Health Monitoring

Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram

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