A channel-dependent algorithm for heart beats detection in ECG recordings

Mondelo, Victor; Lado, Marı́a J.; Méndez, Adriana; Vila, Xosé A.; Rodríguez-Liñares, Leandro

doi:10.23919/cisti.2017.7975975

Cited by 4 publications

(6 citation statements)

References 18 publications

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“…As stated before, we implemented first the standard Pan-Tompkins algorithm to perform single-channel detection, and we validated this algorithm with the MIT-BIH Arrhythmia database. Results obtained with our method were similar to other published results for P+, and slightly better for Se [24].…”

Section: A Single-channel Detectionsupporting

confidence: 90%

“…The single-channel detection algorithm is based on Pan-Tompkins method [9], and it was deeply described in [24]. Briefly, it consists in adapting the single-channel Pan-Tompkins detection algorithm to the different leads of a multichannel ECG recording, to overcome the limitations when detecting beats due to high level of noise existing in the recordings, the missing of some part of the ECG signals, or the impossibility to use adequate preprocessing methods that improve the quality of the original signal.…”

Section: A Single-channel Detectionmentioning

confidence: 99%

“…By adapting the thresholds, we were able to improve the sensitivity Se (1) and the positive predictivity P+ (2) of the beat sequences, specially on channels where the standard Pan-Tompkins algorithm failed to detect many QRS complexes. Using the training set of the St. Petersburg database, we obtained a set of values for T that optimize sensitivity and positive predictivity [24].…”

Section: A Single-channel Detectionmentioning

confidence: 99%

“…However, some false positives appear due to the decrease in the decision levels. This was easily solved by keeping only those beats whose distance to the previously detected one was greater than a certain value [24].…”

Section: A Single-channel Detectionmentioning

confidence: 99%

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Combining 12-Lead ECG Information for a Beat Detection Algorithm

Mondelo

Lado

Méndez

et al. 2017

JADI

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This paper proposes a beat detection algorithm specially tailored to be used with 12 channel ECG records. The algorithm first obtains beat positions on each channel, and then combines this information to get an improved estimate. The detection process involves two stages: 1) single-channel detection: implemented by improving one of the most popular methods (Pan-Tompkins) developed to detect beat positions; and 2) multichannel detection: an algorithm that combines the information of the beat positions obtained in each of the 12 channels. In this way, our results clearly improve those obtained with the single-channel detection method, discarding detection errors, false positives, and duplicated beats. Besides, our single-channel method significantly reduces the temporal error when estimating the positions of QRS complexes. In the multichannel detection, the assessment of our algorithm against one-channel based approaches shows a significant improvement in detection outcome (Se = 99.86%, P+ = 99.98%, RMS RR Interval Error = 25.98 ms, F-Score = 0.9992), making it a good starting point for automatic diagnosis of heart conditions.

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Section: A Single-channel Detectionsupporting

confidence: 90%

Section: A Single-channel Detectionmentioning

confidence: 99%

Section: A Single-channel Detectionmentioning

confidence: 99%

Section: A Single-channel Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Combining 12-Lead ECG Information for a Beat Detection Algorithm

Mondelo

Lado

Méndez

et al. 2017

JADI

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show abstract

“…This method applies over single-channel ECG recordings, and detects heartbeat positions. The method is described in (Mondelo et al, 2017). Briefly, it is divided into two main stages: 1.…”

Section: A Pan-tompkins Algorithmmentioning

confidence: 99%

Detection of Heart Beat Positions in ECG Recordings: A Lead-Dependent Algorithm

Mondelo

Lado

Méndez

et al. 2017

Journal of Information Systems Engineering & Management

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This paper proposes a computerized heartbeat detection method in single-channel electrocardiograms (ECGs). First, the well-known Pan-Tompkins technique was implemented, and next, a channel-dependent version was developed, by adjusting threshold values and reducing false QRS detections. The algorithms were tested with the MIT-BIH Arrhythmia Database (original algorithm), and with the St. Petersburg Database (modified version). When validating the performances of the original Pan-Tompkins algorithm, we have achieved a sensitivity of Se = 99.81, at a positive predictivity (P + ) = 99.85%. The F-Score was 0.9587, and the RMS RR Interval Error (RMSRRIE) resulted to be 4,480.46 ms. When analysing the performance of the modified algorithm, results provided an average value of Se = 99.92%, P + = 99.98%, FScore = 0.9718, and a mean value of 111.05 ms. for the RMSRRIE. In conclusion, the improved PanTompkins algorithm provides higher values for sensitivity and positive predictivity, increased F-Score, and it significantly reduces the temporal error when estimating the positions of QRS complexes. Thus, it could be used as a starting point to detect heartbeat positions in more sophisticated computerized detection systems.

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ECGDT: a graphical software tool for ECG diagnosis

Mondelo,

Lado,

Méndez

2023

Multimed Tools Appl

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While cardiovascular diseases are the leading causes of death in developed countries, detection of cardiac abnormalities can reduce mortality rates, through early and accurate diagnosis. One of the main assets used to help in the diagnosis process is the electrocardiogram (ECG). A free software tool for electrocardiogram analysis and diagnosis is presented. The tool, named ECGDT, allows: (1) to detect beats present on the ECG, both in single and multi-channel levels, (2) to identify beat waves, and (3) to diagnose different cardiac abnormalities. System evaluation was performed in two ways: (1) diagnostic capabilities were tested with Receiver Operating Characteristic (ROC) analysis, and (2) Graphical Software Interface (GUI) aspects, such as attraction, efficiency, or novelty, were evaluated employing User Experience Questionnaire (UEQ) scores. For disease diagnosis, the mean Area Under the ROC Curve (AUC) was 0.821. The system was also capable of detecting 100% of several cardiac abnormalities, such as bradycardia or tachycardia. Related to the GUI, all usability estimators scored values ranged between 2.208 and 2.750 (overall positive evaluations are obtained for values over 0.8). ECGDT could serve as an aid in the diagnosis of different medical abnormalities. In addition, the suitability of the developed interface has been proven.

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A channel-dependent algorithm for heart beats detection in ECG recordings

Cited by 4 publications

References 18 publications

Combining 12-Lead ECG Information for a Beat Detection Algorithm

Combining 12-Lead ECG Information for a Beat Detection Algorithm

Detection of Heart Beat Positions in ECG Recordings: A Lead-Dependent Algorithm

ECGDT: a graphical software tool for ECG diagnosis

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