A Machine Learning Pipeline for Measurement of Arterial Stiffness in A-Mode Ultrasound

Sahani, Ashish Kumar; Srivastava, Divyansh; Sivaprakasam, Mohanasankar; Joseph, Jayaraj

doi:10.1109/tuffc.2021.3109117

Cited by 9 publications

(9 citation statements)

References 16 publications

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“…Table 2 compares the echo pattern recognition inference machines. This work employs a decimated envelope of an A‐mode echo as a feature vector, which improves the inference accuracy with less numbers of feature elements due to a judicious selection of features, compared to the previous works of [9, 10]. Additionally, the proposed approach is purely implemented using HDL, making it easy to implement within an ASIC that incorporates mixed‐signal circuits.…”

Section: Resultsmentioning

confidence: 99%

FPGA based real‐time inference machine for A‐mode ultrasonic echo pattern recognition

Lee

2023

Electronics Letters

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This brief proposes a field programmable gate array (FPGA) based realtime inference machine for A-mode ultrasonic echo pattern recognition. The proposed scheme can aid a probe positioning of single-transducer based ultrasound devices by detecting specific echo patterns on a scanline. The inference machine is based on a combination of a support vector machine and a finite state machine. The proposed inference machine utilizes a minimal pre-processing for a feature extraction, considering a nature of distinctive echo patterns. In addition, a primary computation of linear support vector machine is performed sequentially, and the finite state machine for robust binary classification is relatively compact. As a result, the overall structure of the inference machine can be concisely implemented on FPGA. The proposed inference machine was assembled with a customized A-mode ultrasound scanner. The maximum utilization percentage of look-up-tables in FPGA was less than 1.5%. In evaluation of inferences, the resultant sensitivity and the specificity were 96.2% and 99.87%, respectively.

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Section: Resultsmentioning

confidence: 99%

FPGA based real‐time inference machine for A‐mode ultrasonic echo pattern recognition

Lee

2023

Electronics Letters

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“…Among the sources used for this review, a number were unrelated to any sub-field of computer vision and relied on different sensing schemes from LiDar [ 171 ] to ultrasound [ 13 ] for gathering training data and implementation, in applications from waste management [ 148 ] to heart monitoring [ 13 ]. While the sensing scheme and overall application of these models vastly differed from one another, their numbers for each application and sensor were not sufficient for a proper basis-by-basis comparison.…”

Section: Application Based System Comparisonmentioning

confidence: 99%

“…Most of the papers reviewed in this work utilized some form of computer vision, mainly in areas such as obstacle detection for autonomous vehicles (such as speed bumps) [ 11 ] or safety and security measures (such as violent assault identification) [ 12 ]. However, several also presented embedded machine learning methods for medical applications (such as patient heart monitoring) [ 13 ] or automating more aspects of city management (such as managing the direction and flow of vehicular traffic) [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Biglari

Tang

2023

Sensors

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Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have been steady advances in the performance, memory, and power consumption of embedded devices, most machine learning algorithms still have a very high power consumption and computational demand, making the implementation of embedded machine learning somewhat difficult. However, different devices can be implemented for different applications based on their overall processing power and performance. This paper presents an overview of several different implementations of machine learning on embedded systems divided by their specific device, application, specific machine learning algorithm, and sensors. We will mainly focus on NVIDIA Jetson and Raspberry Pi devices with a few different less utilized embedded computers, as well as which of these devices were more commonly used for specific applications in different fields. We will also briefly analyze the specific ML models most commonly implemented on the devices and the specific sensors that were used to gather input from the field. All of the papers included in this review were selected using Google Scholar and published papers in the IEEExplore database. The selection criterion for these papers was the usage of embedded computing systems in either a theoretical study or practical implementation of machine learning models. The papers needed to have provided either one or, preferably, all of the following results in their studies—the overall accuracy of the models on the system, the overall power consumption of the embedded machine learning system, and the inference time of their models on the embedded system. Embedded machine learning is experiencing an explosion in both scale and scope, both due to advances in system performance and machine learning models, as well as greater affordability and accessibility of both. Improvements are noted in quality, power usage, and effectiveness.

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“…Verma et al [ 115 ] detected coronary artery disease. The study of Yu et al [ 118 ] was the only proposal to classify stroke, while only Rodriguez et al [ 117 ] classified saturated oxygen and Sahani et al [ 119 ] focused on carotid disease. Ying et al [ 116 ], Sivapalan et al [ 121 ], and Rahman et al [ 120 ] focused on ECG abnormalities and ECG noise.…”

Section: Research On Cvd Detection Using Iot/iomtmentioning

confidence: 99%

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

Cuevas-Chávez,

Hernández,

Ortiz-Hernandez

et al. 2023

Healthcare

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According to the Pan American Health Organization, cardiovascular disease is the leading cause of death worldwide, claiming an estimated 17.9 million lives each year. This paper presents a systematic review to highlight the use of IoT, IoMT, and machine learning to detect, predict, or monitor cardiovascular disease. We had a final sample of 164 high-impact journal papers, focusing on two categories: cardiovascular disease detection using IoT/IoMT technologies and cardiovascular disease using machine learning techniques. For the first category, we found 82 proposals, while for the second, we found 85 proposals. The research highlights list of IoT/IoMT technologies, machine learning techniques, datasets, and the most discussed cardiovascular diseases. Neural networks have been popularly used, achieving an accuracy of over 90%, followed by random forest, XGBoost, k-NN, and SVM. Based on the results, we conclude that IoT/IoMT technologies can predict cardiovascular diseases in real time, ensemble techniques obtained one of the best performances in the accuracy metric, and hypertension and arrhythmia were the most discussed diseases. Finally, we identified the lack of public data as one of the main obstacles for machine learning approaches for cardiovascular disease prediction.

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A Machine Learning Pipeline for Measurement of Arterial Stiffness in A-Mode Ultrasound

Cited by 9 publications

References 16 publications

FPGA based real‐time inference machine for A‐mode ultrasonic echo pattern recognition

FPGA based real‐time inference machine for A‐mode ultrasonic echo pattern recognition

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

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