A Modular Low-Complexity ECG Delineation Algorithm for Real-Time Embedded Systems

Bote, Jose Manuel; Recas, J.; Rincon, Francisco; Atienza, David; Hermida, R.

doi:10.1109/jbhi.2017.2671443

Cited by 70 publications

(43 citation statements)

References 25 publications

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“…The ME±ESTD achieved was -2.6±10.8 ms and 4.4±15.2 ms, for the onset and offset respectively. Whereas, [3] reported ME±ESTD of 4.6±7.7 ms and 0.8±8.7 ms for the onset and offset, respectively; and [6] reported 7.0±4.3 ms and -5.0±9.9 ms for the onset and offset, respectively.…”

Section: Resultsmentioning

confidence: 85%

“…The reference approaches are single-lead delineators [3], [6]. The performances of these methods in the QTDB were calculated from predicting for each lead independently and, later, fusing both outcomes by choosing the predicted value with less error for each timepoint.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, some automatic delineation methods have been proposed in the literature [2]- [9]. While there exist both, rule-based [2]- [6] and machine learning-based QRS delineators [7]- [9], the state-of-theart are the former [3], [6]. However, these methods lack adaptability to significant differences in the target scenario since the rules composing them have been carefully designed by experts to match a specific domain, for example, a database comprising ECG signals recorded from athletes while exercising.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning Based QRS Multilead Delineator in Electrocardiogram Signals

Camps¹,

Rodríguez²,

Mincholé³

2018

2018 Computing in Cardiology Conference (CinC)

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The surface electrocardiogram (ECG) is the most widely adopted test to diagnose cardiac diseases. Extracting critical biomarkers from these signals, such as the QRS width, requires delineating the fundamental waves in them. However, even though ECG signals significantly change depending on the recording methodology and cardiac condition, the available QRS delineators are hard to adapt to non-considered cases. We present a deep learning-based multilead ECG delineation method which can successfully delineate QRS complexes. Our approach reached root-mean-square errors (RMSE) of 12.1±0.5 and 18.5±1.1 ms for QRS onset and offset, respectively, when evaluated on the QT database; thus, demonstrating to be comparable to the state-of-the-art. Moreover, these results are similar to the RMSE calculated from differences between the two cardiologists that annotated this database, namely, 14.7 ms for the QRS onset and 17.2 ms for the offset.

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Section: Resultsmentioning

confidence: 85%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning Based QRS Multilead Delineator in Electrocardiogram Signals

Camps¹,

Rodríguez²,

Mincholé³

2018

2018 Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

show abstract

“…Portable and wearable monitoring devices for ECG, also called ECG patch monitoring (EPM), cleared by FDA are limited by recording capabilities such as being only a single lead. AD8232 with three leads, Zio Patch [132], Sensium Life Pebble and two-channel Shimmer3 [133] which is a Bluetooth based wireless sensor are a few popular devices that are used as sensors attached to the body surface for ECG acquisition, detection of the QRS complex [134], [135] and sending the ECG signal to the coordinator. 2) Layer 2: Coordinator: Devices such as Arduino, ADuCM361 and TI MSP430 controllers receive the data from previous layers using directly attached cables or over radio protocols such as wireless IEEE802.11x, Bluetooth IEEE802.15.1, and Zigbee IEEE802.15.4 [136] for graphical representation, which then sends the data to the next layer over a data network such as GSM.…”

Section: ) System 1: As Shown Inmentioning

confidence: 99%

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

et al. 2020

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Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this paper. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined. INDEX TERMS ECG analysis, cardiac arrhythmias, QRS and ST detection, ECG classification, deep learning.

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“…In recent years, personal wearable devices have been introduced as cost-effective solutions for continuous ECG monitoring in daily life. Previous works include ECG monitoring solutions that are low-power but only extract the R peak or the QRS complex and do not further process the ECG signal [3]- [6]. Many others analyze the signal and perform ECG classification, but transmit the signal to a connected smartphone or a remote cloud server for performing the computations associated with ECG classification algorithms [7], [8].…”

Section: Introductionmentioning

confidence: 99%

ECG Classification Algorithm Based on STDP and R-STDP Neural Networks for Real-Time Monitoring on Ultra Low-Power Personal Wearable Devices

Amirshahi

Hashemi

2019

IEEE Trans. Biomed. Circuits Syst.

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This paper presents a novel ECG classification algorithm for real-time cardiac monitoring on ultra low-power wearable devices. The proposed solution is based on spiking neural networks which are the third generation of neural networks. In specific, we employ spike-timing dependent plasticity (STDP), and reward-modulated STDP (R-STDP), in which the model weights are trained according to the timings of spike signals, and reward or punishment signals. Experiments show that the proposed solution is suitable for real-time operation, achieves comparable accuracy with respect to previous methods, and more importantly, its energy consumption is significantly smaller than previous neural network based solutions.

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A Modular Low-Complexity ECG Delineation Algorithm for Real-Time Embedded Systems

Cited by 70 publications

References 25 publications

Deep Learning Based QRS Multilead Delineator in Electrocardiogram Signals

Deep Learning Based QRS Multilead Delineator in Electrocardiogram Signals

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

ECG Classification Algorithm Based on STDP and R-STDP Neural Networks for Real-Time Monitoring on Ultra Low-Power Personal Wearable Devices

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