Simple microprocessor-based system for on-line e.c.g. arrhythmia analysis

Morizet‐Mahoudeaux, Pierre; Moreau, Christian; Moreau, D.; Quarante, J. J.

doi:10.1007/bf02441319

Cited by 32 publications

(14 citation statements)

References 8 publications

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“…This section presents several signal processing techniques [23]–[26] that have been used to emphasise the QRS segment in time, frequency and time-frequency series, as shown in Table 1. Figure 2 demonstrates the importance of the QRS enhancement stage as a prerequisite for detecting the QRS complex.…”

Section: Qrs Enhancementmentioning

confidence: 99%

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Revisiting QRS Detection Methodologies for Portable, Wearable, Battery-Operated, and Wireless ECG Systems

et al. 2014

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Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.

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Section: Qrs Enhancementmentioning

confidence: 99%

“…Different amplitude thresholds have also been used. Moriet-Mahoudeaux et al [23] developed a QRS detector using , which means that values below 30% of the maximum positive signal amplitude is truncated from the signal, while Fraden and Neuman [24] used .…”

Section: Qrs Enhancementmentioning

confidence: 99%

Revisiting QRS Detection Methodologies for Portable, Wearable, Battery-Operated, and Wireless ECG Systems

et al. 2014

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“…The offsetting step has been used in the literature as the last stage for most of the event detection algorithms [41,42,43,44,45,46,47]. The performance of the offsetting approach will be affected by low SNR signals [4,48].…”

Section: Discussionmentioning

confidence: 99%

TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach

Elgendi

2016

Biosensors

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Biomedical signals contain features that represent physiological events, and each of these events has peaks. The analysis of biomedical signals for monitoring or diagnosing diseases requires the detection of these peaks, making event detection a crucial step in biomedical signal processing. Many researchers have difficulty detecting these peaks to investigate, interpret and analyze their corresponding events. To date, there is no generic framework that captures these events in a robust, efficient and consistent manner. A new method referred to for the first time as two event-related moving averages (“TERMA”) involves event-related moving averages and detects events in biomedical signals. The TERMA framework is flexible and universal and consists of six independent LEGO building bricks to achieve high accuracy detection of biomedical events. Results recommend that the window sizes for the two moving averages (W1 and W2) have to follow the inequality (8×W1)≥W2≥(2×W1). Moreover, TERMA is a simple yet efficient event detector that is suitable for wearable devices, point-of-care devices, fitness trackers and smart watches, compared to more complex machine learning solutions.

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“…One of these techniques is the amplitude threshold which has been used by Morizet-Mahoudeaux et al (1981). With this technique, the signal noise is not properly removed and it is usually followed by the first derivative of the ECG signal (Morizet-Mahoudeaux et al, 1981). Other technique is the first derivative of the ECG signal followed by threshold (Okada, 1979), this technique helps to reduce baseline drifts and motion artifacts (Zhang & Lian, 2007); however it does not remove high frequency noise.…”

Section: Introductionmentioning

confidence: 97%

“…In some researches, this stage is described as pre-processing or feature extraction. One of these techniques is the amplitude threshold which has been used by Morizet-Mahoudeaux et al (1981). With this technique, the signal noise is not properly removed and it is usually followed by the first derivative of the ECG signal (Morizet-Mahoudeaux et al, 1981).…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Electrocardiogram Signals by Applying Adaptive Threshold and Principal Component Analysis

Rodriguez¹,

Mexicano²,

Bíla³

et al. 2015

Journal of Applied Research and Technology

103

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This paper presents a novel approach for QRS complex detection and extraction of electrocardiogram signals for different types of arrhythmias. Firstly, the ECG signal is filtered by a band pass filter, and then it is differentiated. After that, the Hilbert transform and the adaptive threshold technique are applied for QRS detection. Finally, the Principal Component Analysis is implemented to extract features from the ECG signal. Nineteen different records from the MIT-BIH arrhythmia database have been used to test the proposed method. A 96.28% of sensitivity and a 99.71% of positive predictivity are reported in this testing for QRS complexity detection, being a positive result in comparison with recent researches. All Rights Reserved

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Simple microprocessor-based system for on-line e.c.g. arrhythmia analysis

Cited by 32 publications

References 8 publications

Revisiting QRS Detection Methodologies for Portable, Wearable, Battery-Operated, and Wireless ECG Systems

Revisiting QRS Detection Methodologies for Portable, Wearable, Battery-Operated, and Wireless ECG Systems

TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach

Feature Extraction of Electrocardiogram Signals by Applying Adaptive Threshold and Principal Component Analysis

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