Robust Heartbeat Detection From Multimodal Data via CNN-Based Generalizable Information Fusion

Bollepalli, Sandeep Chandra; Sastry, C. S.; Jana, Soumya

doi:10.1109/tbme.2018.2854899

Cited by 87 publications

(79 citation statements)

References 26 publications

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“…On the other hand, more advanced neural network based match filters have been developed. These references [60,61,62,63,64,65,66,67,68] contain neural network based match filters. The frequency filters allow for decomposition of signals based on frequency and time.…”

Section: Resultsmentioning

confidence: 99%

“…Techniques used in QRS complex detection range from signal derivative and digital filters [43,44,45,46,47], wavelet transforms [48,49,50,51,52], Hilbert transforms [53,54,55], matched filters [56,57], compressed sensing [58,59], to machine learning and neural networks (NN) approaches [60,61,62,63,64,65,66,67,68]. Among the many classical derivative and digital filter algorithms after the first Pan and Tompkins method [43], GQRS [47] is a simple one with superior performance by using adaptive search intervals and amplitude thresholds.…”

Section: Chapter 2 Inter-patient Cnn-lstm Ecg Qrs Complex Detectionmentioning

confidence: 99%

“…In this section, the following related QRS complex detection algorithms are presented: Pan and Tompkins [43], GQRS [47], Wavedet [48], Xiang et al's CNN [65], and Chandra et al's CNN [68]. The advantages and disadvantages of each algorithm are also described.…”

Section: Related Qrs Complex Detection Algorithmsmentioning

confidence: 99%

“…Chandra et al's paper [68] uniquely features an inter-patient testing scheme. In the testing scheme, the patients in the training set differ from the patients in the testing set.…”

Section: Cnn-based Generalizable Information Fusionmentioning

confidence: 99%

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Applications of Machine Learning

Rudström¹

2020

Algorithms for Intelligent Systems

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In this thesis, many machine learning algorithms were applied to electrocardiogram (ECG), spectral analysis, and Field Programmable Gate Arrays (FPGAs). In ECG, QRS complexes are useful for measuring the heart rate and for the segmentation of ECG signals. QRS complexes were detected using WaveletCNN Autoencoder filters and ConvLSTM detectors. The WaveletCNN Autoencoders filters the ECG signals using the wavelet filters, while the ConvLSTM detects the spatial temporal patterns of the QRS complexes. For the spectral analysis topic, the detection of chemical compounds using spectral analysis is useful for identifying unknown substances. However, spectral analysis algorithms require vast amounts of data. To solve this problem, B-spline neural networks were developed for the generation of infrared and ultraviolet/visible spectras. This allowed for the generation of large training datasets from a few experimental measurements. Graphical Processing Units (GPUs) are good for training and testing neural networks. However, using multiple GPUs together is hard because PCIe bus is not suited for scattering operations and reduce operations. FPGAs are more flexible as they can be arranged in a mesh or toroid or hypercube configuration on the PCB. These configurations provide higher data throughput and results in faster computations. A general neural network framework was written in VHDL for Xilinx FPGAs. It allows for any neural network to be trained or tested on FPGAs.

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Section: Resultsmentioning

confidence: 99%

Section: Chapter 2 Inter-patient Cnn-lstm Ecg Qrs Complex Detectionmentioning

confidence: 99%

Section: Related Qrs Complex Detection Algorithmsmentioning

confidence: 99%

“…Chandra et al's paper [68] uniquely features an inter-patient testing scheme. In the testing scheme, the patients in the training set differ from the patients in the testing set.…”

Section: Cnn-based Generalizable Information Fusionmentioning

confidence: 99%

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Applications of Machine Learning

Rudström¹

2020

Algorithms for Intelligent Systems

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“…Several papers proposed data fusion techniques that identified reliable signals and distinguished relative importance among different signals. Chandra et al [7], selected as a best paper, demonstrate an innovative way of signal fusion deploying convolutional neural networks (CNNs) for robust heartbeat detection. In the monitoring of cardiovascular diseases, especially in critical-care situations, accurate heartbeat detection is a key, but often prone to errors when monitoring with one physiological signal.…”

Section: Emerging Trend 1: Selective Fusion Of Multiple Signals/ Domamentioning

confidence: 99%

Notable Papers and Trends from 2019 in Sensors, Signals, and Imaging Informatics

Hsu¹,

Baumgartner

Deserno

2020

Yearb Med Inform

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Objective: To highlight noteworthy papers that are representative of 2019 developments in the fields of sensors, signals, and imaging informatics. Method: A broad literature search was conducted in January 2020 using PubMed. Separate predefined queries were created for sensors/signals and imaging informatics using a combination of Medical Subject Heading (MeSH) terms and keywords. Section editors reviewed the titles and abstracts of both sets of results. Papers were assessed on a three-point Likert scale by two co-editors, rated from 3 (do not include) to 1 (should be included). Papers with an average score of 2 or less were then read by all three section editors, and the group nominated top papers based on consensus. These candidate best papers were then rated by at least six external reviewers. Results: The query related to signals and sensors returned a set of 255 papers from 140 unique journals. The imaging informatics query returned a set of 3,262 papers from 870 unique journals. Based on titles and abstracts, the section co-editors jointly filtered the list down to 50 papers from which 15 candidate best papers were nominated after discussion. A composite rating after review determined four papers which were then approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. These best papers represent different international groups and journals. Conclusions: The four best papers represent state-of-the-art approaches for processing, combining, and analyzing heterogeneous sensor and imaging data. These papers demonstrate the use of advanced machine learning techniques to improve comparisons between images acquired at different time points, fuse information from multiple sensors, and translate images from one modality to another.

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Joint Exploitation of Hemodynamic and Electrocardiographic Signals by Hidden Markov Models for Heartbeat Detection

Monroy

Altuve

2019

IFMBE Proceedings

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Robust Heartbeat Detection From Multimodal Data via CNN-Based Generalizable Information Fusion

Abstract: In medical signal monitoring systems, our technique would accurately estimate heartbeat locations even when only a subset of channels are reliable.

Cited by 87 publications

References 26 publications

Applications of Machine Learning

Applications of Machine Learning

Notable Papers and Trends from 2019 in Sensors, Signals, and Imaging Informatics

Joint Exploitation of Hemodynamic and Electrocardiographic Signals by Hidden Markov Models for Heartbeat Detection

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