A Machine Learning Approach for Heart Rate Estimation from PPG Signal using Random Forest Regression Algorithm

Bashar, Shikder Shafiul; Miah, Md. Sazal; Karim, A.H.M. Zadidul; Mahmud, Md. Abdullah Al; Hasan, Zahid

doi:10.1109/ecace.2019.8679356

Cited by 31 publications

(16 citation statements)

References 8 publications

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“…They are typically developed and implemented on computer systems to perform specific tasks effectively based on pattern recognition and statistical inference (Bishop 2006). Notable applications of machine learning include image classification (Lu and Weng 2007;Sudharshan et al 2019), regression (Huang et al 2011;Bashar and Mahmud 2019), and face detection and recognition (Campadelli et al 2004;Ranjan et al 2019). Furthermore, another application area of machine learning is Natural Language Processing (NLP).…”

Section: What Is Machine Learning?mentioning

confidence: 99%

Extracting Natech Reports from Large Databases: Development of a Semi-Intelligent Natech Identification Framework

Luo

Cruz

Tzioutzios

2020

Int J Disaster Risk Sci

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Natural hazard-triggered technological accidents (Natechs) refer to accidents involving releases of hazardous materials (hazmat) triggered by natural hazards. Huge economic losses, as well as human health and environmental problems are caused by Natechs. In this regard, learning from previous Natechs is critical for risk management. However, due to data scarcity and high uncertainty concerning such hazards, it becomes a serious challenge for risk managers to detect Natechs from large databases, such as the National Response Center (NRC) database. As the largest database of hazmat release incidents, the NRC database receives hazmat release reports from citizens in the United States. However, callers often have incomplete details about the incidents they are reporting. This results in many records having incomplete information. Consequently, it is quite difficult to identify and extract Natechs accurately and efficiently. In this study, we introduce machine learning theory into the Natech retrieving research, and a Semi-Intelligent Natech Identification Framework (SINIF) is proposed in order to solve the problem. We tested the suitability of two supervised machine learning algorithms, namely the Long Short-Term Memory (LSTM) and the Convolutional Neural Network (CNN), and selected the former for the development of the SINIF. According to the results, the SINIF is efficient (a total number of 826,078 records were analyzed) and accurate (the accuracy is over 0.90), while 32,841 Natech reports between 1990 and 2017 were extracted from the NRC database. Furthermore, the majority of those Natech reports (97.85%) were related to meteorological phenomena, with hurricanes (24.41%), heavy rains (19.27%), and storms (18.29%) as the main causes of these reported Natechs. Overall, this study suggests that risk managers can benefit immensely from SINIF in analyzing Natech data from large databases efficiently.

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Section: What Is Machine Learning?mentioning

confidence: 99%

Extracting Natech Reports from Large Databases: Development of a Semi-Intelligent Natech Identification Framework

Luo

Cruz

Tzioutzios

2020

Int J Disaster Risk Sci

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“…By collecting ECG signals from fabric-based chest straps with dry electrodes, it is proved that the cosine-based adaptive algorithm behaves better in eliminating high-amplitude motion artifact noise. Another new approach to estimate heart rate without motion effects is called the multi-model machine learning approach (MMMLA) applied by Bashar et al [ 82 ]. It works like this: firstly it trains and tests the model for the different feature and different data set, then separates noisy and non-noisy data by K-means clustering, which lets the machine learn data separately ( Figure 6 b).…”

Section: Bioelectric Signal Monitoringmentioning

confidence: 99%

“… System improvement: ( a ) zoomed-up image of a pixel integrated with four strain sensors, two temperature sensors, and a fingerprint like structure © 2019 IEEE; ( b ) comparison between estimated and given true heart rate for noisy data for matched case using random forest regression with all model features [ 82 ] © 2019 IEEE; ( c ) the whole process of handling the flexible and stretchable electrodes [ 64 ] © 2014 IEEE; ( d ) wearable ECG monitoring device. …”

Section: Figurementioning

confidence: 99%

Recent Progress in Flexible Wearable Sensors for Vital Sign Monitoring

Liu

Bai

et al. 2020

Sensors

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With the development of flexible electronic materials, as well as the wide development and application of smartphones, the cloud, and wireless systems, flexible wearable sensor technology has a significant and far-reaching impact on the realization of personalized medical care and the reform of the consumer market in the future. However, due to the high requirements for accuracy, reliability, low power consumption, and less data error, the development of these potential areas is full of challenges. In order to solve these problems, this review mainly searches the literature from 2008 to May 2020, based on the PRISMA process. Based on them, this paper reviews the latest research progress of new flexible materials and different types of sensors for monitoring vital signs (including electrophysiological signals, body temperature, and respiratory frequency) in recent years. These materials and sensors can help realize accurate signal detection based on comfortable and sustainable observation, and may likely be applied to future daily clothing.

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“…Furthermore, the algorithm will work for creating a complete forest by repeating the previous steps. Then during the prediction process, the algorithm tries to combine the trees using estimated outcome and voting procedure [36]. The purpose of merging the random trees through voting in a forest is to opt out the highest forecasted tree, which can enhance the prediction accuracy for future data.…”

Section: Random Forestmentioning

confidence: 99%

Implementation of Machine Learning Model to Predict Heart Failure Disease

Alotaibi¹

2019

IJACSA

174

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In the current era, Heart Failure (HF) is one of the common diseases that can lead to dangerous situation. Every year almost 26 million of patients are affecting with this kind of disease. From the heart consultant and surgeon's point of view, it is complex to predict the heart failure on right time. Fortunately, classification and predicting models are there, which can aid the medical field and can illustrates how to use the medical data in an efficient way. This paper aims to improve the HF prediction accuracy using UCI heart disease dataset. For this, multiple machine learning approaches used to understand the data and predict the HF chances in a medical database. Furthermore, the results and comparative study showed that, the current work improved the previous accuracy score in predicting heart disease. The integration of the machine learning model presented in this study with medical information systems would be useful to predict the HF or any other disease using the live data collected from patients.

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A Machine Learning Approach for Heart Rate Estimation from PPG Signal using Random Forest Regression Algorithm

Cited by 31 publications

References 8 publications

Extracting Natech Reports from Large Databases: Development of a Semi-Intelligent Natech Identification Framework

Extracting Natech Reports from Large Databases: Development of a Semi-Intelligent Natech Identification Framework

Recent Progress in Flexible Wearable Sensors for Vital Sign Monitoring

Implementation of Machine Learning Model to Predict Heart Failure Disease

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