ECG frame classification using dynamic time warping

Huang, Bin; Kinsner, W.

doi:10.1109/ccece.2002.1013101

Cited by 63 publications

(32 citation statements)

References 6 publications

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“…Related work in the time alignment of ECGs also exists. Dynamic time warping has been a popular technique in ECG frame classification [10], and more recently, in the recognition of heart beat patterns for synthetically generated signals [17]. In all such alignments, however, the amplitude of the signal was used rather than a detailed modeling of the shape.…”

Section: Related Workmentioning

confidence: 99%

Shape-based Matching of ECG Recordings

Syeda-Mahmood

Beymer

Wang

2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-An electrocardiogram (ECG) is an important and commonly used diagnostic aid in cardiovascular disease diagnosis. Physicians routinely perform diagnosis by a simple visual examination of ECG waveform shapes. In this paper, we address the problem of shape-based retrieval of ECG recordings, both digital and scanned from paper, to infer similarity in diagnosed diseases. Specifically, we use the knowledge of ECG recording structure to segment and extract curves representing various recording channels from ECG images. We then present a method of capturing the perceptual shape similarity of ECG waveforms by combining shape matching with dynamic time warping. The shape similarity of each recording channel is combined to develop an overall shape similarity measure between ECG recordings. Results are presented that demonstrate the method on shape-based matching of various cardiovascular diseases.

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Section: Related Workmentioning

confidence: 99%

Shape-based Matching of ECG Recordings

Syeda-Mahmood

Beymer

Wang

2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…Supposing that the filtered raw signal is , the amplitude of the reference signal is the amplitude of the filtered raw signal estimated by (17) The frequency is estimated by the well-known zero-across detection method [32]. It finds the zero crossing points in a predetermined time interval and counts the number of cycles that occur in the time interval to obtain the frequency estimation as equation (18). In this study, the length of the measured raw signal is 50,000 and sampling frequency is 24.3 kHz, which means the frequency resolution is 0.486 and it is sufficient for the frequency estimation.…”

Section: Figure 4 a Flow Diagram Of The Proposed Data Analysismentioning

confidence: 99%

“…It was popularised in the '70s, when it was mainly applied to isolated word recognition and speech recognition [11,[15][16][17] to account for differences in speaking rates between speakers and utterances. Since then, it has been employed for clustering and classification in countless domains: electro-cardiogram analysis [18][19][20], clustering of gene expression profiles [21,22], biometrics [23,24], process monitoring [25]. Moreover, DTW has been also used in handwriting and online signature matching [10], sign language recognition and gesture recognition, data mining and time series clustering, computer vision and computer animation, surveillance, protein sequence alignment and chemical engineering, music and signal processing [26].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

Zhen

Wang

et al. 2013

Mechanical Systems and Signal Processing

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Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency components with the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal, allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time domain in comparison with conventional FT based methods.

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“…Bellman, R. and Kalaba, R. first introduced it on adaptive control processes [10]. Then it has been popularized in the application of word and speed recognition [7,[11][12][13], electro-cardiogram analysis [14][15][16], clustering of gene expression profiles [17,18], biometrics [19,20], process monitoring [21], and data mining and time series clustering [7,22]. Recently, Zhen, D. et al have explored it in processing signals from motors for condition monitoring and shown promising results in that the aligned signals by DTW do not lose information and is suitable for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Phase-compensation-based dynamic time warping for fault diagnosis using the motor current signal

Zhen¹,

Zhao²,

Gu³

et al. 2012

Meas. Sci. Technol.

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Abstract. Dynamic Time Warping (DTW) is a time domain based method and widely used in various similar recognition and data mining applications. This paper presents a phase compensation based DTW to process the motor current signals for detecting and quantifying various faults in a two-stage reciprocating compressor under different operating conditions. DTW is an effective method to align up two signals for dissimilarity analysis. However, it has drawbacks such as singularities and high computational demands that limit its application in processing motor current signals for obtaining modulation characteristics accurately in diagnosing compressor faults. Therefore, a phase compensation approach is developed to reduce the singularity effect and a sliding window is designed to improve computing efficiency. Based on the proposed method, the motor current signals measured from the compressor induced with different common faults are analysed for fault diagnosis. Results show that residual signal analysis using the phase compensation based DTW allows the fault related sideband features to be resolved more accurately for obtaining reliable fault detection and diagnosis. It provides an effective and easy approach to the analysis of motor current signals for better diagnosis in the time domain in comparison with conventional Fourier Transform based methods.

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ECG frame classification using dynamic time warping

Cited by 63 publications

References 6 publications

Shape-based Matching of ECG Recordings

Shape-based Matching of ECG Recordings

Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

Phase-compensation-based dynamic time warping for fault diagnosis using the motor current signal

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