Time‐Frequency Methods for Non‐stationary Statistical Signal Processing

Hlawatsch, Franz; Matz, Gerald

doi:10.1002/9780470611203.ch10

Cited by 15 publications

(31 citation statements)

References 58 publications

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“…The main objective of time-frequency analysis is to formulate a function which can describe the signal energy density simultaneously in both time and frequency. This can be manipulated for use in a wide range of applications that have a time-varying spectrum [23]. The use of time-frequency distribution is crucial for non-stationary and multi-component ECG signals [29].…”

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

confidence: 99%

“…Sharma et al [18]; or frame-based techniques (which consider a few successive beats for short periods, usually up to 5 s), such as those described by CC Hsu [19], Jen Hong Tan et al [20], U. Rajendra Acharya et al [21], and Vidya K. Sudarshan et al [22]. Of late, however, several studies have shown that the heart problem detection rate can be improved by analysing a long series of beats (more than a few seconds) [23]. This is a significant revelation in terms of early heart disease detection, where the capacity to swiftly identify and recover from abnormal heart activity is vital.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

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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“…To facilitate further analysis of the EFIM in (17), we define the signal energy P b , the root-mean-square (RMS) bandwidth W b , and the RMS integration time T b for the signal g b (t) as [62],…”

Section: Performance Limit Using Received Signals (2) and (3)mentioning

confidence: 99%

Fundamental Limits of Self-localization for Cooperative Robotic Platforms Using Signals of Opportunity

Leng

Tay

2015

Cooperative Robots and Sensor Networks 2015

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A fundamental problem in robotic applications is the localization of the robots. We consider the problem of global self-localization for a robotic platform with autonomous robots using signals of opportunity (SOOP). We first give a brief overview of the state-of-the-art in robotic localization using SOOP, and then propose a scheme that requires minimal prior environmental information, no pre-configuration, and only loose synchronization between the robots. To further analyze the potential for the use of SOOP in robotic localization and to investigate the effect of clock asynchronism, we derive an analytical expression for the equivalent Fisher information matrix of the Cramér-Rao lower bound (CRLB). The derivation is based on the received signal waveform, and allows us to analyze the contributions of various factors to the localization accuracy. The CRLB provides a valuable guideline for the design of a robotic platform in which a desired level of localization accuracy is to be achieved. We also analyze the distortions in the time difference of arrival and frequency difference of arrival measurements caused by different clock offsets and skews at the robots. We propose a robust algorithm to estimate robot location and velocity, which mitigates the clock biases. Simulation results suggest that our proposed algorithm approaches the CRLB when clock skews have small standard deviations.

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“…As every quadratic distribution, the spectrogram presents interference terms given by S x1,x2 (t, ν), but these terms are negligible if x1(t) and x2(t) are sufficiently distant, as is justified in [3]. This property is a consequence of the spectrogram poor resolution.…”

Section: Introductionmentioning

confidence: 97%