Spectral method for processing signals of a high-accuracy laser radar

Klimov, A. V.; Главный, В. Г.; Bakakin, G. V.; Meledin, V. G.

doi:10.3103/s8756699016060066

Cited by 1 publication

(1 citation statement)

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“…To illustrate the advantage of the frame blocking for pretreatment of the data, the performance of the proposed model was compared with that using a common preprocessing method (28)(29)(30), which uses direct cutting and zero-padding protocol to unify the length of ECG signals. As for a fixed length of 40-s ECG data (i.e., 20,000 sampling data points), the common method can either truncate the exceeding signal samples when the length of original records exceeds 40 s or pads zeros to the data when the length is < 40 s.…”

Section: Performance On Different Preprocessingmentioning

confidence: 99%

Automatic Detection for Multi-Labeled Cardiac Arrhythmia Based on Frame Blocking Preprocessing and Residual Networks

Zhang

2021

Front. Cardiovasc. Med.

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Introduction: Electrocardiograms (ECG) provide information about the electrical activity of the heart, which is useful for diagnosing abnormal cardiac functions such as arrhythmias. Recently, several algorithms based on advanced structures of neural networks have been proposed for auto-detecting cardiac arrhythmias, but their performance still needs to be further improved. This study aimed to develop an auto-detection algorithm, which extracts valid features from 12-lead ECG for classifying multiple types of cardiac states.Method: The proposed algorithm consists of the following components: (i) a preprocessing component that utilizes the frame blocking method to split an ECG recording into frames with a uniform length for all considered ECG recordings; and (ii) a binary classifier based on ResNet, which is combined with the attention-based bidirectional long-short term memory model.Result: The developed algorithm was trained and tested on ECG data of nine types of cardiac states, fulfilling a task of multi-label classification. It achieved an averaged F1-score and area under the curve at 0.908 and 0.974, respectively.Conclusion: The frame blocking and bidirectional long-short term memory model represented an improved algorithm compared with others in the literature for auto-detecting and classifying multi-types of cardiac abnormalities.

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Section: Performance On Different Preprocessingmentioning

confidence: 99%

Automatic Detection for Multi-Labeled Cardiac Arrhythmia Based on Frame Blocking Preprocessing and Residual Networks

Zhang

2021

Front. Cardiovasc. Med.

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show abstract

Spectral method for processing signals of a high-accuracy laser radar

Cited by 1 publication

References 3 publications

Automatic Detection for Multi-Labeled Cardiac Arrhythmia Based on Frame Blocking Preprocessing and Residual Networks

Automatic Detection for Multi-Labeled Cardiac Arrhythmia Based on Frame Blocking Preprocessing and Residual Networks

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