A deep learning approach for ECG-based heartbeat classification for arrhythmia detection

Sannino, Giovanna; Pietro, Giuseppe De

doi:10.1016/j.future.2018.03.057

Cited by 370 publications

(191 citation statements)

References 33 publications

Supporting

Mentioning

165

Contrasting

Unclassified

Order By: Relevance

“…Few electrodes are placed on certain standardized parts of the body skin in order to record ECG signals, which are basically an electric potential difference between two electrodes. The combination of two electrodes which the electric signal measures forms an imaginary line in the body called a lead (or channel) [182], as shown in Figure 2. From an electrophysiological point of view, the ECG consists of a set of characteristic deflections, so-called waves, which appear near-periodically in time, and each of them is related to one of the basic phases of the cardiac cycle.…”

Section: Ecg Fundamentals and Databasesmentioning

confidence: 99%

“…In addition, finally, the T wave can be observed and brings information about the ventricular repolarization phase and the myocardial tissue recovery. Very infrequently, a U wave can also be detected in the end [182]. One cardiac cycle consists of a P, Q, R, S, T and U wave, corresponding to one ECG beat.…”

Section: Ecg Fundamentals and Databasesmentioning

confidence: 99%

“…The novelty of this work is that ECG segments of variable length were used, achieving 98.10% accuracy, 97.50% sensitivity and 98.70% specificity for ten-fold cross validation strategy. A simpler solution was implemented [182] in terms of a common Feed-Forward Neural Network approach for ECG beat classification, conducting the experiments on the MIT-BIH Arrhythmia Database, and, although a very simple architecture was used, good results were obtained in terms of efficiency, accuracy, sensitivity, and specificity. Diabetes detection has been also addressed as another task derived from ECG signals.…”

Section: Recent Interest In Deep Learning For Ecgmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

View full text Add to dashboard Cite

In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.

show abstract

Section: Ecg Fundamentals and Databasesmentioning

confidence: 99%

Section: Ecg Fundamentals and Databasesmentioning

confidence: 99%

Section: Recent Interest In Deep Learning For Ecgmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In this way, we can correctly support a proper radio coverage needed to implement the described case studies. In these contexts, the BE-GTW architecture will be used to monitor the mental stress by acquiring and analysing Heart Rate Variability (HRV) signals [17], [18], as discussed in detail in the following section. HRV analysis in based on the study of the beat-to-beat variations in the heart rate.…”

Section: Case Studies Descriptionmentioning

confidence: 99%

An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0

Pace

Aloi

Gravina

et al. 2019

IEEE Trans. Ind. Inf.

330

117

View full text Add to dashboard Cite

Edge computing paradigm has attracted many interests in the last few years as a valid alternative to the standard Cloud-based approaches to reduce the interaction timing and the huge amount of data coming from IoT devices toward the Internet. In the next future, Edge-based approaches will be essential to support time-dependent applications in the Industry 4.0 context; thus, the paper proposes BodyEdge, a novel architecture well suited for human-centric applications, in the context of the emerging healthcare industry. It consists of a tiny mobile client module and a performing Edge gateway supporting multi-radio and multi-technology communication to collect and locally process data coming from different scenarios; moreover, it also exploits the facilities made available from both private and public Cloud platforms to guarantee a high flexibility, robustness and adaptive service level. The advantages of the designed software platform have been evaluated in terms of reduced transmitted data and processing time through a real implementation on different hardware platforms. The conducted study also highlighted the network conditions (data load and processing delay) in which BodyEdge is a valid and inexpensive solution for healthcare application scenarios.

show abstract

“…Therefore, a Big data technique is introduced in this work to meet the challenges faced by classify the ECG beats. Recently, deep learning techniques have been used by many companies, including Facebook, Google, IBM, Microsoft, NEC, Netflix, and NVIDIA [7,8], and in a very large set of application domains such as customer churn prediction in telecom company [9]. In this paper, a novel deep learning approach for ECG beats classification is presented.…”

mentioning

confidence: 99%

Analysis and classification of heart diseases using heartbeat features and machine learning algorithms

2019

View full text Add to dashboard Cite

A deep learning approach for ECG-based heartbeat classification for arrhythmia detection

Cited by 370 publications

References 33 publications

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0

Analysis and classification of heart diseases using heartbeat features and machine learning algorithms

Contact Info

Product

Resources

About