Performance Evaluation of Time-Frequency Distributions for ECG Signal Analysis

Hussein, Ahmed Faeq; Hashim, Shaiful Jahari; Aziz, Ahmad Fazli Abdul; Rokhani, Fakhrul Zaman; Adnan, Wan Azizun Wan

doi:10.1007/s10916-017-0871-8

Cited by 23 publications

(17 citation statements)

References 58 publications

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“…Many types of Cohen's class-based distributions are employed to analyse the biosignal. As for ECG analysis, the Choi-Williams distribution delivers reliable and highly accurate results with the capacity to find the middle ground between unwanted cross-terms and the intensity of the Time-Frequency Distribution (TFD) [35].…”

Section: Choi-williams Time-frequency Distribution (Cwd)mentioning

confidence: 99%

An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and Choi-Williams Time-Frequency Analysis Incorporating a Multi-Class SVM Classifier

Hussein

Hashim

Rokhani

et al. 2021

Sensors

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Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital for the prevention of medical complications related to the heart, as they can lead to heart muscle death (known as myocardial infarction). The electrocardiogram (ECG) reflects these cardiac condition changes as electrical signals. However, an accurate interpretation of these waveforms still calls for the expertise of an experienced cardiologist. Several algorithms have been developed to overcome issues in this area. In this study, a new scheme for myocardial ischemia detection with multi-lead long-interval ECG is proposed. This scheme involves an observation of the changes in ischemic-related ECG components (ST segment and PR segment) by way of the Choi-Williams time-frequency distribution to extract ST and PR features. These extracted features are mapped to a multi-class SVM classifier for training in the detection of unknown conditions to determine if they are normal or ischemic. The use of multi-lead ECG for classification and 1 min intervals instead of beats or frames contributes to improved detection performance. The classification process uses the data of 92 normal and 266 patients from four different databases. The proposed scheme delivered an overall result with 99.09% accuracy, 99.49% sensitivity, and 98.44% specificity. The high degree of classification accuracy for the different and unknown data sources used in this study reflects the flexibility, validity, and reliability of this proposed scheme. Additionally, this scheme can assist cardiologists in detecting signal abnormality with robustness and precision, and can even be used for home screening systems to provide rapid evaluation in emergency cases.

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Section: Choi-williams Time-frequency Distribution (Cwd)mentioning

confidence: 99%

An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and Choi-Williams Time-Frequency Analysis Incorporating a Multi-Class SVM Classifier

Hussein

Hashim

Rokhani

et al. 2021

Sensors

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“…A common method to perform spatial frequency analysis is the wavelet transform. Equation 1 [31] presents the 1D continuous wavelet transform (CWT).…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

An Adaptive Biomedical Data Managing Scheme Based on the Blockchain Technique

et al. 2019

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A crucial role is played by personal biomedical data when it comes to maintaining proficient access to health records by patients as well as health professionals. However, it is difficult to get a unified view pertaining to health data that have been scattered across various health centers/hospital sections. To be specific, health records are distributed across many places and cannot be integrated easily. In recent years, blockchain has arisen as a promising solution that helps to achieve the sharing of individual biomedical information in a secure way, whilst also having the benefit of privacy preservation because of its immutability. This research puts forward a blockchain-based managing scheme that helps to establish interpretation improvements pertaining to electronic biomedical systems. In this scheme, two blockchains were employed to construct the base, whereby the second blockchain algorithm was used to generate a secure sequence for the hash key that was generated in first blockchain algorithm. This adaptive feature enables the algorithm to use multiple data types and also combines various biomedical images and text records. All data, including keywords, digital records, and the identity of patients, are private key encrypted with a keyword searching function so as to maintain data privacy, access control, and a protected search function. The obtained results, which show a low latency (less than 750 ms) at 400 requests/second, indicate the possibility of its use within several health care units such as hospitals and clinics.

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“…Various studies adopt TFDs to improve the performance of conventional signal processing methods in different areas such as biomedicine [18], [19], radar [20], [21] and telecommunication [22], [23]. Currently, the TFD performance is optimized and evaluated using TF measures such as ratio of norms [24], energy concentration [25], entropy [26], and application-specific criterion [27]. Energy concentration is used in [28] to optimize the Spectrogram based on the short-time fractional Fourier transform.…”

Section: Introductionmentioning

confidence: 99%

Design of an Optimal Piece-Wise Spline Wigner-Ville Distribution for TFD Performance Evaluation and Comparison

Al-Sa'D

Boashash

Gabbouj

2021

IEEE Trans. Signal Process.

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This paper proposes a new performance evaluation process for time-frequency distributions (TFD) by designing a reference optimal TFD and novel accuracy and resolution measures. The motivation comes from the need for a TFD performance evaluation method that is objective, capable of quantifying the TFD accuracy and resolution, can determine the performance difference among different TFDs, and suitable for signals with an arbitrary number of components, instantaneous frequency and amplitude. We formulate the proposed optimal TFD, namely the piece-wise spline Wigner-Ville distribution (PW-WVD), by decomposing a standard non-stationary signal model using piece-wise linear frequency modulated (LFM) basis and by exploiting the Wigner-Ville distribution optimality for LFM signals. We compare the designed PW-WVD to conventional optimal TFDs and show that the former is more suitable to serve as a reference for TFD performance evaluation. Using the PW-WVD we derive TFD accuracy and resolution measures, compare them to conventional approaches, and analyze their sensitivity to form a TFD selection criteria. We evaluate the accuracy and resolution of twelve different TFDs and develop precise TFD selection strategies with or without prior information on the signal parameters. Results indicate that the compact kernel distribution is the best performing TFD given no prior information on the signal parameters and different TFDs must be selected upon the availability of prior information.

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Performance Evaluation of Time-Frequency Distributions for ECG Signal Analysis

Cited by 23 publications

References 58 publications

An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and Choi-Williams Time-Frequency Analysis Incorporating a Multi-Class SVM Classifier

An Automated High-Accuracy Detection Scheme for Myocardial Ischemia Based on Multi-Lead Long-Interval ECG and Choi-Williams Time-Frequency Analysis Incorporating a Multi-Class SVM Classifier

An Adaptive Biomedical Data Managing Scheme Based on the Blockchain Technique

Design of an Optimal Piece-Wise Spline Wigner-Ville Distribution for TFD Performance Evaluation and Comparison

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