A multiple signal classification approach for photoplethysmography signals in healthy and athletic subjects

Al–Fahoum, Amjed S.; Al-Zaben, Awad; Seafan, Waseem

doi:10.1504/ijbet.2015.066962

Cited by 15 publications

(9 citation statements)

References 30 publications

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“…The filtered signal is used for calculation of all parameters’ values for hand movement MNAs detection. The DWT was applied to non-stationary signals to effectively get AR parameters [43-45]. In this paper, as shown in Fig.…”

Section: Materials and Preprocessingmentioning

confidence: 99%

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A Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signals

et al. 2018

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Photoplethysmography (PPG) is a technique to detect blood volume changes in an optical way. Representative PPG applications are the measurements of oxygen saturation, heart rate, and respiratory rate. However, PPG signals are sensitive to motion and noise artifacts (MNAs) especially when they are obtained from smartphone cameras. Moreover, PPG signals are different among users and each individual’s PPG signal has a unique characteristic. Hence, an effective MNA detection and reduction method for smartphone PPG signals, which adapts itself to each user in a personalized way, is highly demanded. Here, a concept of the probabilistic neural network (PNN) is introduced to be used with the proposed extracted parameters. The signal amplitude, standard deviation of peak to peak time intervals and amplitudes, along with the mean of moving standard deviation, signal slope changes, and the optimal autoregressive (AR) model order are proposed for effective MNA detection. Accordingly, the performance of the proposed personalized algorithm is compared with conventional MNA detection algorithms. As performance metrics, we considered accuracy, sensitivity, and specificity. The results show that the overall performance of the personalized MNA detection is enhanced compared to the generalized algorithm. The average values of the accuracy, sensitivity and specificity of the personalized one are 98.07%, 92.6%, and 99.78%, respectively, while these are 89.92%, 84.21%, and 93.63% for the general one.

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Section: Materials and Preprocessingmentioning

confidence: 99%

“…In this paper, as shown in Fig. 2, the high pass filter output is processed by discrete wavelet transform (DWT) for the AR model parameter calculation since AR parameter requires more detailed time-varying frequency information, the DWT is additionally applied only to get the AR parameter more effectively [43, 45]. Fig.…”

Section: Materials and Preprocessingmentioning

confidence: 99%

A Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signals

et al. 2018

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“…The core problem is to design efficient software or develop more advanced data processing methods for how to organize and understand biological data more effectively [ 16 ]. At present, the research work of bioinformatics mainly focuses on gene sequence analysis, biodiversity measurement, protein structure prediction, and protein and gene expression analysis [ 17 ]. Gene sequence analysis, for example, phage Φ - X174, is the first measured complete genome sequences of an organism.…”

Section: Proposed Methodsmentioning

confidence: 99%

Classification and Reconstruction of Biomedical Signals Based on Convolutional Neural Network

Zhu

Chen

Xie

et al. 2022

Computational Intelligence and Neuroscience

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The efficient biological signal processing method can effectively improve the efficiency of researchers to explore the work of life mechanism, so as to better reveal the relationship between physiological structure and function, thus promoting the generation of major biological discoveries; high-precision medical signal analysis strategy can, to a certain extent, share the pressure of doctors’ clinical diagnosis and assist them to formulate more favorable plans for disease prevention and treatment, so as to alleviate patients’ physical and mental pain and improve the overall health level of the society. This article in biomedical signal is very representative of the two types of signals: mammary gland molybdenum target X-ray image (mammography) and the EEG signal as the research object, combined with the deep learning field of CNN; the most representative model is two kinds of biomedical signal classification, and reconstruction methods conducted a series of research: (1) a new classification method of breast masses based on multi-layer CNN is proposed. The method includes a CNN feature representation network for breast masses and a feature decision mechanism that simulates the physician’s diagnosis process. By comparing with the objective classification accuracy of other methods for the identification of benign and malignant breast masses, the method achieved the highest classification accuracy of 97.0% under different values of c and gamma, which further verified the effectiveness of the proposed method in the identification of breast masses based on molybdenum target X-ray images. (2) An EEG signal classification method based on spatiotemporal fusion CNN is proposed. This method includes a multi-channel input classification network focusing on spatial information of EEG signals, a single-channel input classification network focusing on temporal information of EEG signals, and a spatial-temporal fusion strategy. Through comparative experiments on EEG signal classification tasks, the effectiveness of the proposed method was verified from the aspects of objective classification accuracy, number of model parameters, and subjective evaluation of CNN feature representation validity. It can be seen that the method proposed in this paper not only has high accuracy, but also can be well applied to the classification and reconstruction of biomedical signals.

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“…PPG has been widely accepted by the International Organization for Standardization (ISO) and the European Committee for Oxygen Saturation Measurement. When a finger is placed on the PPG device, it can easily record a high-quality pulse signal 5 – 8 . Pulse signals from PPG were preprocessed to improve signal quality and estimate heart rate 9 – 11 .…”

Section: Introductionmentioning

confidence: 99%

Research on recognition and classification of pulse signal features based on EPNCC

Chen

Guo

Wang

et al. 2022

Sci Rep

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To rapidly obtain the complete characterization information of pulse signals and to verify the sensitivity and validity of pulse signals in the clinical diagnosis of related diseases. In this paper, an improved PNCC method is proposed as a supplementary feature to enable the complete characterization of pulse signals. In this paper, the wavelet scattering method is used to extract time-domain features from impulse signals, and EEMD-based improved PNCC (EPNCC) is used to extract frequency-domain features. The time–frequency features are mixed into a convolutional neural network for final classification and recognition. The data for this study were obtained from the MIT-BIH-mimic database, which was used to verify the effectiveness of the proposed method. The experimental analysis of three types of clinical symptom pulse signals showed an accuracy of 98.3% for pulse classification and recognition. The method is effective in complete pulse characterization and improves pulse classification accuracy under the processing of the three clinical pulse signals used in the paper.

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A multiple signal classification approach for photoplethysmography signals in healthy and athletic subjects

Cited by 15 publications

References 30 publications

A Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signals

A Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signals

Classification and Reconstruction of Biomedical Signals Based on Convolutional Neural Network

Research on recognition and classification of pulse signal features based on EPNCC

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