Respiratory rate extraction from single-lead ECG using homomorphic filtering

Sharma, Hemant; Sharma, Kamalesh Kumar; Bhagat, Om Lata

doi:10.1016/j.compbiomed.2015.01.024

Cited by 23 publications

(8 citation statements)

References 18 publications

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“…While another research using ECG and acceleration signals only achieved the accuracy of 74.5% [18]. Since the RRV based features can be extracted from ECG signals [19], the sleep staging methods involved HRV and RRV features can be simply implemented by using single-lead ECG signals. In general, the current research of sleep staging with ECG signals has two disadvantages.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Class Automatic Sleep Staging Method Based on Long Short-Term Memory Network Using Single-Lead Electrocardiogram Signals

et al. 2019

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To overcome the disadvantage of clinical manual sleep staging, a convenient, economical, and efficient multi-class automatic sleep staging method is proposed based on long short-term memory network (LSTM) using single-lead electrocardiogram signals. From electrocardiogram signals, heart rate variability and respiratory signals were calculated, and, then, totally 25 features were extracted. Four different classifiers, including the two-class classifier to distinguish between wake and sleep, the three-class classifier to distinguish wake, non-rapid eye movement sleep, and rapid eye movement, the four-class classifier to distinguish wake, light sleep, slow wave sleep, and rapid eye movement, and the five-class classifier to distinguish wake, sleep stage N1, sleep stage N2, sleep stage N3, and rapid eye movement, were constructed using the LSTM. The single-lead electrocardiogram data from 238 patients with full sleep stages during sleep were used for the training set and the data from other 60 patients were regarded as a validation set. The rest of 75 patients have left aside for testing set. The accuracy of two-class, three-class, four-class, and five-class sleep staging was 89.84%, 84.07%, 77.76%, and 71.16% and the Cohen's kappa statistic k was 0.52, 0.58, 0.55, and 0.52, respectively, which realized the moderate agreement with clinical analysis. When expanding the dataset to extra 1068 patients with missing sleep stages, the accuracy has no obvious reduction but the Cohen's kappa statistic k dropped to 0.51, 0.52, 0.48, and 0.43, respectively. The proposed method, in this paper, is promising for low-cost, efficient, and convenient sleep staging in home care monitoring. INDEX TERMS Electrocardiogram, heart rate variability, long short-term memory, sleep staging.

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Section: Introductionmentioning

confidence: 99%

A Multi-Class Automatic Sleep Staging Method Based on Long Short-Term Memory Network Using Single-Lead Electrocardiogram Signals

et al. 2019

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“…We have noted that in the proposed CLSTM algorithm, the output of the temporal convolutional layers and the LSTM layers are very similar to the features extracted by traditional methods [26,38,45,56]. These layers can collectively extract the features closely related to the data, and using a large number of convolutional kernels, the model can be regarded as extracting the essential characteristics of more categories.…”

Section: The Output Of the Middle Layersmentioning

confidence: 91%

“…Both phases are important to the performance of the HSS tasks. A variety of feature extraction methods have been developed to extract the useful features of heart sounds in recent decades, such as the homomorphic envelope features, the energy envelope feature, the Hilbert envelope features, the wavelet envelope features, the spectral features, and the power spectral density (PSD) envelope features [4,8,10,26,38,41,44,45,[47][48][49]51,56,65]. Figure 2 illustrates some envelope features that are often used in heart segmentation algorithms.…”

Section: Introductionmentioning

confidence: 99%

End-to-end heart sound segmentation using deep convolutional recurrent network

Yao

Sun

et al. 2021

Complex Intell. Syst.

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Heart sound segmentation (HSS) aims to detect the four stages (first sound, systole, second heart sound and diastole) from a heart cycle in a phonocardiogram (PCG), which is an essential step in automatic auscultation analysis. Traditional HSS methods need to manually extract the features before dealing with HSS tasks. These artificial features highly rely on extraction algorithms, which often result in poor performance due to the different operating environments. In addition, the high-dimension and frequency characteristics of audio also challenge the traditional methods in effectively addressing HSS tasks. This paper presents a novel end-to-end method based on convolutional long short-term memory (CLSTM), which directly uses audio recording as input to address HSS tasks. Particularly, the convolutional layers are designed to extract the meaningful features and perform the downsampling, and the LSTM layers are developed to conduct the sequence recognition. Both components collectively improve the robustness and adaptability in processing the HSS tasks. Furthermore, the proposed CLSTM algorithm is easily extended to other complex heart sound annotation tasks, as it does not need to extract the characteristics of corresponding tasks in advance. In addition, the proposed algorithm can also be regarded as a powerful feature extraction tool, which can be integrated into the existing models for HSS. Experimental results on real-world PCG datasets, through comparisons to peer competitors, demonstrate the outstanding performance of the proposed algorithm.

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“…Chest volume variations change the area enclosed by the loop, creating an opposing proportional current [ 29 ]. Beside these three primary methods, other technologies are being used to get respiratory waveform: accelerometers [ 30 ]; extracted from the ECG signal [ 31 ]; derived from pulse oximetry [ 32 ] polymer-based transducers sensors [ 33 ]; optical fibers [ 34 ]; etc. Al-Khalidi in 2011 [ 35 ] has made a deep review about the methods used to measure respiration rate.…”

Section: Valuable Vital Signsmentioning

confidence: 99%

Wearable Health Devices—Vital Sign Monitoring, Systems and Technologies

Dias

Cunha

2018

Sensors

662

361

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Wearable Health Devices (WHDs) are increasingly helping people to better monitor their health status both at an activity/fitness level for self-health tracking and at a medical level providing more data to clinicians with a potential for earlier diagnostic and guidance of treatment. The technology revolution in the miniaturization of electronic devices is enabling to design more reliable and adaptable wearables, contributing for a world-wide change in the health monitoring approach. In this paper we review important aspects in the WHDs area, listing the state-of-the-art of wearable vital signs sensing technologies plus their system architectures and specifications. A focus on vital signs acquired by WHDs is made: first a discussion about the most important vital signs for health assessment using WHDs is presented and then for each vital sign a description is made concerning its origin and effect on heath, monitoring needs, acquisition methods and WHDs and recent scientific developments on the area (electrocardiogram, heart rate, blood pressure, respiration rate, blood oxygen saturation, blood glucose, skin perspiration, capnography, body temperature, motion evaluation, cardiac implantable devices and ambient parameters). A general WHDs system architecture is presented based on the state-of-the-art. After a global review of WHDs, we zoom in into cardiovascular WHDs, analysing commercial devices and their applicability versus quality, extending this subject to smart t-shirts for medical purposes. Furthermore we present a resumed evolution of these devices based on the prototypes developed along the years. Finally we discuss likely market trends and future challenges for the emerging WHDs area.

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Respiratory rate extraction from single-lead ECG using homomorphic filtering

Cited by 23 publications

References 18 publications

A Multi-Class Automatic Sleep Staging Method Based on Long Short-Term Memory Network Using Single-Lead Electrocardiogram Signals

A Multi-Class Automatic Sleep Staging Method Based on Long Short-Term Memory Network Using Single-Lead Electrocardiogram Signals

End-to-end heart sound segmentation using deep convolutional recurrent network

Wearable Health Devices—Vital Sign Monitoring, Systems and Technologies

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