Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals

Tan, Jen Hong; Hagiwara, Yuki; Pang, Winnie; Lim, I. Im S.; Oh, Shu Lih; Adam, Muhammad; Tan, Ru San; Chen, Ming; Acharya, U. Rajendra

doi:10.1016/j.compbiomed.2017.12.023

Cited by 303 publications

(148 citation statements)

References 24 publications

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“…Neural networks and machine learning algorithms have been successfully utilized across multiple medical specialties, including ophthalmology, cardiology, and oncology . The accuracy of these algorithms for specific tasks often approaches and sometimes exceeds that of physicians.…”

Section: Introductionmentioning

confidence: 99%

Otoscopic diagnosis using computer vision: An automated machine learning approach

Livingstone

Chau²

2019

The Laryngoscope

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Objective: Access to otolaryngology is limited by lengthy wait lists and lack of specialists, especially in rural and remote areas. The objective of this study was to use an automated machine learning approach to build a computer vision algorithm for otoscopic diagnosis capable of greater accuracy than trained physicians. This algorithm could be used by primary care providers to facilitate timely referral, triage, and effective treatment.Methods: Otoscopic images were obtained from Google Images (Google Inc., Mountain View, CA), from open access repositories, and within otolaryngology clinics associated with our institution. After preprocessing, 1,366 unique images were uploaded to the Google Cloud Vision AutoML platform (Google Inc.) and annotated with one or more of 14 otologic diagnoses. A consensus set of labels for each otoscopic image was attained, and a multilabel classifier architecture algorithm was trained. The performance of the algorithm on an 89-image test set was compared to the performance of physicians from pediatrics, emergency medicine, otolaryngology, and family medicine.Results: For all diagnoses combined, the average precision (positive predictive value) of the algorithm was 90.9%, and the average recall (sensitivity) was 86.1%. The algorithm made 79 correct diagnoses with an accuracy of 88.7%. The average physician accuracy was 58.9%.Conclusion: We have created a computer vision algorithm using automated machine learning that on average rivals the accuracy of the physicians we tested. Fourteen different otologic diagnoses were analyzed. The field of medicine will be changed dramatically by artificial intelligence within the next few decades, and physicians of all specialties must be prepared to guide that process.

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Section: Introductionmentioning

confidence: 99%

Otoscopic diagnosis using computer vision: An automated machine learning approach

Livingstone

Chau²

2019

The Laryngoscope

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“…A deep learning approach was recently applied to extracting ECG features and yielded higher accuracy than state-of-art feature extraction methods. The convolutional neural network (CNN) is a deep learning method that enables short-term 10 s ECG analysis [15,16,25]. In the simulations of the present study, the features of the ECG signal were extracted with the CNN algorithm.…”

Section: Feature Equationmentioning

confidence: 99%

“…The area, normalized decay, line length, mean energy, average peak amplitude, average valley amplitude, and normalized peak number are the main time-domain features of an EEG signal. ECG signals are widely utilized to predict arrhythmia, coronary artery disease, and paroxysmal atrial fibrillation [15][16][17]. Chui et al [18] and Sahayadhas et al [19] considered ECG signals for estimating the drowsiness condition by extracting heart rate variability (HRV) information, which includes timeand frequency-domain analysis.…”

Section: Introductionmentioning

confidence: 99%

Optimal Feature Search for Vigilance Estimation Using Deep Reinforcement Learning

et al. 2020

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A low level of vigilance is one of the main reasons for traffic and industrial accidents. We conducted experiments to evoke the low level of vigilance and record physiological data through single-channel electroencephalogram (EEG) and electrocardiogram (ECG) measurements. In this study, a deep Q-network (DQN) algorithm was designed, using conventional feature engineering and deep convolutional neural network (CNN) methods, to extract the optimal features. The DQN yielded the optimal features: two CNN features from ECG and two conventional features from EEG. The ECG features were more significant for tracking the transitions within the alertness continuum with the DQN. The classification was performed with a small number of features, and the results were similar to those from using all of the features. This suggests that the DQN could be applied to investigating biomarkers for physiological responses and optimizing the classification system to reduce the input resources.

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“…LSTM network was proposed by the German researchers Hochreiter and Schmidhuber as a solution to the long-term dependencies problem [9]. Due to the special network architecture, LSTM networks have great learning ability in dealing with time-series forecasting in many applications, such as speech recognition [10], stock price volatility [11], sentiment analysis [12], traffic forecast [13] and disease diagnosis [14]. Few attempts have been made to apply LSTM networks to hydrological problems [15].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification in Machine Learning Modeling for Multi-Step Time Series Forecasting: Example of Recurrent Neural Networks in Discharge Simulations

Song

Ding

Liu

et al. 2020

Water

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As a revolutionary tool leading to substantial changes across many areas, Machine Learning (ML) techniques have obtained growing attention in the field of hydrology due to their potentials to forecast time series. Moreover, a subfield of ML, Deep Learning (DL) is more concerned with datasets, algorithms and layered structures. Despite numerous applications of novel ML/DL techniques in discharge simulation, the uncertainty involved in ML/DL modeling has not drawn much attention, although it is an important issue. In this study, a framework is proposed to quantify uncertainty contributions of the sample set, ML approach, ML architecture and their interactions to multi-step time-series forecasting based on the analysis of variance (ANOVA) theory. Then a discharge simulation, using Recurrent Neural Networks (RNNs), is taken as an example. Long Short-Term Memory (LSTM) network, a state-of-the-art DL approach, was selected due to its outstanding performance in time-series forecasting, and compared with simple RNN. Besides, novel discharge forecasting architecture is designed by combining the expertise of hydrology and stacked DL structure, and compared with conventional design. Taking hourly discharge simulations of Anhe (China) catchment as a case study, we constructed five sample sets, chose two RNN approaches and designed two ML architectures. The results indicate that none of the investigated uncertainty sources are negligible and the influence of uncertainty sources varies with lead-times and discharges. LSTM demonstrates its superiority in discharge simulations, and the ML architecture is as important as the ML approach. In addition, some of the uncertainty is attributable to interactions rather than individual modeling components. The proposed framework can both reveal uncertainty quantification in ML/DL modeling and provide references for ML approach evaluation and architecture design in discharge simulations. It indicates uncertainty quantification is an indispensable task for a successful application of ML/DL.

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Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals

Cited by 303 publications

References 24 publications

Otoscopic diagnosis using computer vision: An automated machine learning approach

Otoscopic diagnosis using computer vision: An automated machine learning approach

Optimal Feature Search for Vigilance Estimation Using Deep Reinforcement Learning

Uncertainty Quantification in Machine Learning Modeling for Multi-Step Time Series Forecasting: Example of Recurrent Neural Networks in Discharge Simulations

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