Heartbeat classification in wearables using multi-layer perceptron and time-frequency joint distribution of ECG

Das, Anup; Catthoor, Francky; Schaafsma, Siebren

doi:10.1145/3278576.3278598

Cited by 36 publications

(20 citation statements)

References 19 publications

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“…We evaluate 10 machine learning programs which are representative of three most commonly-used neural network classes: convolutional neural network (CNN), multi-layer perceptron (MLP), and recurrent neural network (RNN). These applications are 1) LeNet based handwritten digit recognition with 28 × 28 images of handwritten digits from the MNIST dataset; 2) AlexNet for ImageNet classiication; 3) VGG16, also for ImageNet classiication; 4) ECG-based heart-beat classiication (HeartClass) [9,43] using electrocardiogram (ECG) data; 5) image smoothing (ImgSmooth) [28] on 64 × 64 images; 6) edge detection (EdgeDet) [28] on 64 × 64 images using diference-of-Gaussian; 7) multi-layer perceptron (MLP)-based handwritten digit recognition (DigitRecogMLP) [50] using the MNIST database; 8) heart-rate estimation (HeartEstm) [31] using ECG data; 9) RNN-based predictive visual pursuit (VisualPursuit) [64]; and 10) recurrent digit recognition (DigitRecogSTDP) [50]. To demonstrate the potential of DFSynthesizer, we consider a real-time neuromorphic system, where these machine learning programs are executed continuously in a streaming fashion.…”

Section: Evaluated Applicationsmentioning

confidence: 99%

DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware

Song

Chong

Balaji

et al. 2022

ACM Trans. Embed. Comput. Syst.

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Spiking Neural Networks (SNN) are an emerging computation model, which uses event-driven activation and bio-inspired learning algorithms. SNN-based machine-learning programs are typically executed on tile-based neuromorphic hardware platforms, where each tile consists of a computation unit called crossbar, which maps neurons and synapses of the program. However, synthesizing such programs on an off-the-shelf neuromorphic hardware is challenging. This is because of the inherent resource and latency limitations of the hardware, which impact both model performance, e.g., accuracy, and hardware performance, e.g., throughput. We propose DFSynthesizer, an end-to-end framework for synthesizing SNN-based machine learning programs to neuromorphic hardware. The proposed framework works in four steps. First, it analyzes a machine-learning program and generates SNN workload using representative data. Second, it partitions the SNN workload and generates clusters that fit on crossbars of the target neuromorphic hardware. Third, it exploits the rich semantics of Synchronous Dataflow Graph (SDFG) to represent a clustered SNN program, allowing for performance analysis in terms of key hardware constraints such as number of crossbars, dimension of each crossbar, buffer space on tiles, and tile communication bandwidth. Finally, it uses a novel scheduling algorithm to execute clusters on crossbars of the hardware, guaranteeing hardware performance. We evaluate DFSynthesizer with 10 commonly used machine-learning programs. Our results demonstrate that DFSynthesizer provides much tighter performance guarantee compared to current mapping approaches.

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Section: Evaluated Applicationsmentioning

confidence: 99%

DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware

Song

Chong

Balaji

et al. 2022

ACM Trans. Embed. Comput. Syst.

Self Cite

View full text Add to dashboard Cite

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“…MLPs have been successfully used in a wide range of application scenarios, such as disease detection [45], activity recognition [46], and brain-machine interface [47]. Many studies identified MLPs to be the best or one of the best algorithms to solve tasks in the IoT domain using wearable devices [48]- [51].…”

Section: Application Showcasesmentioning

confidence: 99%

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

Wang

Magno

Cavigelli

et al. 2020

IEEE Internet Things J.

126

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The growing number of low-power smart devices in the Internet of Things is coupled with the concept of "Edge Computing", that is moving some of the intelligence, especially machine learning, towards the edge of the network. Enabling machine learning algorithms to run on resource-constrained hardware, typically on low-power smart devices, is challenging in terms of hardware (optimized and energy-efficient integrated circuits), algorithmic and firmware implementations. This paper presents FANN-on-MCU, an open-source toolkit built upon the Fast Artificial Neural Network (FANN) library to run lightweight and energy-efficient neural networks on microcontrollers based on both the ARM Cortex-M series and the novel RISC-Vbased Parallel Ultra-Low-Power (PULP) platform. The toolkit takes multi-layer perceptrons trained with FANN and generates code targeted to low-power microcontrollers. This paper also presents detailed analyses of energy efficiency across the different cores, and the optimizations to handle different network sizes. Moreover, it provides a detailed analysis of parallel speedups and degradations due to parallelization overhead and memory transfers. Further evaluations include experimental results for three different applications using a self-sustainable wearable multi-sensor bracelet. Experimental results show a measured latency in the order of only a few microseconds and power consumption of a few milliwatts while keeping the memory requirements below the limitations of the targeted microcontrollers. In particular, the parallel implementation on the octa-core RISC-V platform reaches a speedup of 22x and a 69% reduction in energy consumption with respect to a single-core implementation on Cortex-M4 for continuous real-time classification.

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“…In the literature, there are many works related to pattern recognition on ECG signals, in which the heart beat type recognition was explored, as in [7]- [9], for instance. There are many approaches employed to identify the heart beats, but the general methodology is: (1) Acquiring the database;…”

Section: Introductionmentioning

confidence: 99%

Classifying cardiac rhythms by means of digital signal processing and machine learning

Pinho

Gomes

Santos

2020

JCIS

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Electrocardiogram (ECG) measures the electrical activity of the heart, which can be used in the diagnosis of different heart diseases. In the scientific literature there are many studies that have been applied machine learning for recognizing ECG patterns, where most of them attempt to classify heart beats. This paper presents a novel methodology for automatically classifying seventeen cardiac rhythms by means of digital signal processing and machine learning. The steps before the classification include the mapping of ECG signal to the frequency domain through power spectrum density, class balance with Adaptive Synthetic Sampling algorithm, and statistical normalization. The classifiers employed were Support Vector Machine, Multilayer Perceptron Neural Network, k-Nearest Neighbors, and Random Forest. The results showed accuracy, sensitivity, specificity, and Fleiss' kappa of up to 98.86%, 99.93%, 98.85%, and 89.68%, respectively, which are relatively better than the performance observed in the state-of-the-art works. In addition, this study highlighted that when the class balance procedure is applied, the classification step becomes less complex and can increase in terms of performance.

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Heartbeat classification in wearables using multi-layer perceptron and time-frequency joint distribution of ECG

Cited by 36 publications

References 19 publications

DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware

DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

Classifying cardiac rhythms by means of digital signal processing and machine learning

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