FPGA Based Arrhythmia Detection

Kumari, L. V. Rajani; Sai, Y. Padma; Balaji, N.; Viswada, K.

doi:10.1016/j.procs.2015.07.495

Cited by 28 publications

(6 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The hardware implementation of automatic ECG analysis systems is essential for ambulant monitorization of patients, and there are several examples in the literature for both ASIC [23,24] and FPGA [25,26] implementations. However, to the best of our knowledge, there are no hardware implementations of ECG signal processors that apply the Hermite fit for beat compression or classifications.…”

Section: Discussionmentioning

confidence: 99%

“…However, to the best of our knowledge, there are no hardware implementations of ECG signal processors that apply the Hermite fit for beat compression or classifications. For example, the work in [26] describes the implementation of another technique called Empirical Mode Decomposition applied to ECG signals in a Spartan 3E FPGA but does not report power, performance and area metrics. As for the detection performance, the overall accuracy reported is 94.8%, while with Hermite functions, it is possible to achieve 96.66%.…”

Section: Discussionmentioning

confidence: 99%

“…This module can be used as the input to systems to compress the ECG data as well as to classifiers. Despite the interest in producing hardware systems for real-time processing of ECG signals [23][24][25][26], to the best of our knowledge, this is the first time that Hermite function fitting with a complete preprocessing chain is implemented in hardware for ECG processing. The main contributions of this paper are as follows:…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Low-Latency, Low-Power FPGA Implementation of ECG Signal Characterization Using Hermite Polynomials

et al. 2021

View full text Add to dashboard Cite

Automatic ECG signal characterization is of critical importance in patient monitoring and diagnosis. This process is computationally intensive, and low-power, online (real-time) solutions to this problem are of great interest. In this paper, we present a novel, dedicated hardware implementation of the ECG signal processing chain based on Hermite functions, aiming for real-time processing. Starting from 12-bit ADC samples of the ECG signal, the hardware implements filtering, peak and QRS detection, and least-squares Hermite polynomial fit on heartbeats. This hardware module can be used to compress ECG data or to perform beat classification. The hardware implementation has been validated on a Field Programmable Gate Array (FPGA). The implementation is generated using an algorithm-to-hardware compiler tool-chain and the resulting hardware is characterized using a low-cost off-the-shelf FPGA card. The single-beat best-fit computation latency when using six Hermite basis polynomials is under 1 s with a throughput of 3 beats/s and with an average power dissipation around 28 mW, demonstrating true real-time applicability.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Low-Latency, Low-Power FPGA Implementation of ECG Signal Characterization Using Hermite Polynomials

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The biomedical engineering revolution forces researchers to enhance the automatic diagnosis by optimization of ECG processing algorithms in order to ensure real-time monitoring of cardiac data [12][13][14][15][16]; and their implementation on embedded systems as recent technological resources [8,[17][18][19].…”

Section: A Ecg Signal Processingmentioning

confidence: 99%

Speeding up an Adaptive Filter based ECG Signal Pre-processing on Embedded Architectures

Mejhoudi¹,

Latif²,

Saddik³

et al. 2021

IJACSA

View full text Add to dashboard Cite

Medical applications increasingly require complex calculations with constraints of accelerated processing time. These applications are therefore oriented towards the integration of high-performance embedded architectures. In this context, the detection of cardiac abnormalities is a task that remains a high priority in emergency medicine. ECG analysis is a complex task that requires significant computing time since a large amount of information must be analyzed in parallel with high frequencies. Real-time processing is the biggest challenge for researchers, when talking about applications that require time constraints like that of cardiac activity monitoring. This work evaluates the Adaptive Dual Threshold Filter (ADTF) algorithm dedicated to ECG signal filtering using various embedded architectures: A Raspberry 3B+ and Odroid XU4. The implementation has been based on C/C++ and OpenMP to exploit the parallelism in the used architectures. The evaluation was validated using several ECG signals proposed in MIT-BIH Arrhythmia database with a sampling frequency of 360 Hz. Based on an algorithmic complexity study and a parallelization of the functional blocks which present significant workloads, the evaluation results show a mean execution time of 7.5 ms on the Raspberry 3B+ and 0.34 ms on the Odroid XU4. With an efficient parallelization on the Odroid XU4 architecture, real-time performance can be achieved.

show abstract

“…ECG signals are commonly used to diagnose heart problems and sleep-related disorders. Various sources of noise within the signal's frequency band typically contaminate the recorded ECG signal, altering its properties and making it difficult to extract useable information from it [18]- [22]. The characteristics of the ECG signal are critical in identifying heart disorders [23]- [25] and sleep disorders.…”

Section: Introductionmentioning

confidence: 99%

Automated sleep apnea classification based on statistical and spectral analysis of electrocardiogram signals

Kumari

Lohitha²,

Kavya

et al. 2022

IJEECS

View full text Add to dashboard Cite

<span>Well-being sleep is a significant segment for maintaining mental comparably as genuine flourishing. More than six-hour recordings are required to distinguish sleep apnea, which are extremely long duration recordings. It's difficult for a human to deduce the problem from electrocardiogram (ECG) readings. As a result, automated PC-based assessment is expected to detect <span>abnormalities as early as possible. An automated framework for the classification of obstructive sleep apnea (OSA) can moreover be distinguished from the ECG Signals. From the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) polysomnographic informational collection, 18 subjects have been considered as data signals. The signal is segmented into 30 seconds and features are extracted by using the discrete wavelet transform (DWT). DWT of seven-level decomposition is applied on the segmented signal by using</span> the wavelet 'sym3'. 12 features were extracted from each level and all of them are used to categorize the five types of sleep apnea. Random forest, k-nearest neighbor (KNN), and support vector machine (SVM) are used for classification of apnea. The random forest (RF) classifier outperformed the others with an average of accuracy (Acc) of 98.53% according to the study's findings. The experimental results show the developed model outperforms the state of art algorithms in the literature.</span>

show abstract

FPGA Based Arrhythmia Detection

Cited by 28 publications

References 16 publications

A Low-Latency, Low-Power FPGA Implementation of ECG Signal Characterization Using Hermite Polynomials

A Low-Latency, Low-Power FPGA Implementation of ECG Signal Characterization Using Hermite Polynomials

Speeding up an Adaptive Filter based ECG Signal Pre-processing on Embedded Architectures

Automated sleep apnea classification based on statistical and spectral analysis of electrocardiogram signals

Contact Info

Product

Resources

About