Approximate Pruned and Truncated Haar Discrete Wavelet Transform VLSI Hardware for Energy-Efficient ECG Signal Processing

Seidel, Henrique B.; Rosa, Morgana Macedo Azevedo da; Paim, Guilherme; Costa, Eduardo; Almeida, Sérgio; Bampi, Sérgio

doi:10.1109/tcsi.2021.3057584

Cited by 42 publications

(6 citation statements)

References 49 publications

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“…Designers have traditionally turned to field-programmable gate arrays (FPGAs) to accelerate performance in hardware designs for compute-intensive applications such as computer vision, communications, industrial embedded systems, and increasingly the Internet of Things (IoT). Engineers who need to employ complex, compute-intensive algorithms often rely on FPGAs to accelerate execution without compromising tight power budgets [10,11,18]. FPGAs have emerged as a dominant platform for speeding artificial intelligence algorithms in edge-computing systems [14,18,35].…”

Section: Field Programmable Gate Arrays (Fpga)mentioning

confidence: 99%

“…For example, studies have shown a strong correlation between several features obtained from PPG (e.g., pulse rate variability) and similar metrics collected from ECG (e.g., heart rate variability), highlighting the reciprocal information between these two modalities. However, as smartwatches, smartphones, and other similar wearable and mobile devices have advanced, PPG has become the industry standard as a simple, wearable-friendly, and low-cost option for continuous heart rate (HR) monitoring for daily usage [10][11][12]. Nonetheless, PPG has inaccuracies in HR estimates and other limitations compared to standard ECG monitoring equipment, owing to skin tone, varied skin types, motion artifacts, and signal crossover.…”

Section: Introductionmentioning

confidence: 99%

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ECG-ViT: A Transformer-Based ECG Classifier for Energy-Constraint Wearable Devices

et al. 2022

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The advancement in deep learning techniques has helped researchers acquire and process multimodal data signals from different healthcare domains. Now, the focus has shifted towards providing end-to-end solutions, i.e., processing these data and developing models that can be directly implemented on edge devices. To achieve this, the researchers try to solve two problems: (I) reduce the complex feature dependencies and (II) reduce the complexity of the deep learning model without compromising accuracy. In this paper, we focus on the later part of reducing the complexity of the model by using the knowledge distillation framework. We have introduced knowledge distillation on the Vision Transformer model to study the MIT-BIH Arrhythmia Database. A tenfold crossvalidation technique was used to validate the model, and we obtained a 99.7% F1 score and 99.3% accuracy. The model was further tested on the Xilinx Alveo U50 FPGA accelerator, and it is found fit for any low-powered wearable device implementation.

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Section: Field Programmable Gate Arrays (Fpga)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ECG-ViT: A Transformer-Based ECG Classifier for Energy-Constraint Wearable Devices

et al. 2022

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“…Guy J. J. Warmerdam et al [41] developed a multichannel hierarchical probabilistic framework with predictive modeling for fetal ECG Rpeak detection. H. B. Seidel et al [42] proposed using Haar-DWT hardware architecture for energyefficient processing of ECG signals while maintaining an R-peak detection. Saira Aziz et al [43] have tested various machine learning models using the SPH database to classify various classes.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Dataset

Sharma¹,

Eskicioglu²

2022

IJAIA

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The number of IoT devices in healthcare is expected to rise sharply due to increased demand since the COVID-19 pandemic. Deep learning and IoT devices are being employed to monitor body vitals and automate anomaly detection in clinical and non-clinical settings. Most of the current technology requires the transmission of raw data to a remote server, which is not efficient for resource-constrained IoT devices and embedded systems. Additionally, it is challenging to develop a machine learning model for ECG classification due to the lack of an extensive open public database. To an extent, to overcome this challenge PTB-XL dataset has been used. In this work, we have developed machine learning models to be deployed on Raspberry Pi. We present an evaluation of our TensorFlow Model with two classification classes. We also present the evaluation of the corresponding TensorFlow Lite FlatBuffers to demonstrate their minimal run-time requirements while maintaining acceptable accuracy.

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“…Approximate computing is a new paradigm where an acceptable error is induced in the computing to achieve more energy-efficient processing [ 28 , 29 , 30 , 31 , 32 , 33 ]. It has been introduced at different system levels [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ], and a large number of approximate arithmetic circuits have been designed to save chip area and energy [ 35 , 38 , 46 , 47 , 48 , 49 , 50 , 51 ]. Multiplication is a very common, but expensive operation, with exact multipliers being large circuits that consume a significant amount of energy.…”

Section: Introductionmentioning

confidence: 99%

An Approximate GEMM Unit for Energy-Efficient Object Detection

Pilipović

Risojević

Božič

et al. 2021

Sensors

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Edge computing brings artificial intelligence algorithms and graphics processing units closer to data sources, making autonomy and energy-efficient processing vital for their design. Approximate computing has emerged as a popular strategy for energy-efficient circuit design, where the challenge is to achieve the best tradeoff between design efficiency and accuracy. The essential operation in artificial intelligence algorithms is the general matrix multiplication (GEMM) operation comprised of matrix multiplication and accumulation. This paper presents an approximate general matrix multiplication (AGEMM) unit that employs approximate multipliers to perform matrix–matrix operations on four-by-four matrices given in sixteen-bit signed fixed-point format. The synthesis of the proposed AGEMM unit to the 45 nm Nangate Open Cell Library revealed that it consumed only up to 36% of the area and 25% of the energy required by the exact general matrix multiplication unit. The AGEMM unit is ideally suited to convolutional neural networks, which can adapt to the error induced in the computation. We evaluated the AGEMM units’ usability for honeybee detection with the YOLOv4-tiny convolutional neural network. The results implied that we can deploy the AGEMM units in convolutional neural networks without noticeable performance degradation. Moreover, the AGEMM unit’s employment can lead to more area- and energy-efficient convolutional neural network processing, which in turn could prolong sensors’ and edge nodes’ autonomy.

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Approximate Pruned and Truncated Haar Discrete Wavelet Transform VLSI Hardware for Energy-Efficient ECG Signal Processing

Cited by 42 publications

References 49 publications

ECG-ViT: A Transformer-Based ECG Classifier for Energy-Constraint Wearable Devices

ECG-ViT: A Transformer-Based ECG Classifier for Energy-Constraint Wearable Devices

Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Dataset

An Approximate GEMM Unit for Energy-Efficient Object Detection

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