scholarly journals A Continuous Cuffless Blood Pressure Estimation Using Tree-Based Pipeline Optimization Tool

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 686
Author(s):  
Suliman Mohamed Fati ◽  
Amgad Muneer ◽  
Nur Arifin Akbar ◽  
Shakirah Mohd Taib
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Blood Pressure Monitoring ◽  
Notch Filter ◽  
Absolute Error ◽  
Skin Surface ◽  
Central Line ◽  
Band Pass Filter ◽  
K Nearest Neighbors ◽  
Pass Filter

High blood pressure (BP) may lead to further health complications if not monitored and controlled, especially for critically ill patients. Particularly, there are two types of blood pressure monitoring, invasive measurement, whereby a central line is inserted into the patient’s body, which is associated with infection risks. The second measurement is cuff-based that monitors BP by detecting the blood volume change at the skin surface using a pulse oximeter or wearable devices such as a smartwatch. This paper aims to estimate the blood pressure using machine learning from photoplethysmogram (PPG) signals, which is obtained from cuff-based monitoring. To avoid the issues associated with machine learning such as improperly choosing the classifiers and/or not selecting the best features, this paper utilized the tree-based pipeline optimization tool (TPOT) to automate the machine learning pipeline to select the best regression models for estimating both systolic BP (SBP) and diastolic BP (DBP) separately. As a pre-processing stage, notch filter, band-pass filter, and zero phase filtering were applied by TPOT to eliminate any potential noise inherent in the signal. Then, the automated feature selection was performed to select the best features to estimate the BP, including SBP and DBP features, which are extracted using random forest (RF) and k-nearest neighbors (KNN), respectively. To train and test the model, the PhysioNet global dataset was used, which contains 32.061 million samples for 1000 subjects. Finally, the proposed approach was evaluated and validated using the mean absolute error (MAE). The results obtained were 6.52 mmHg for SBS and 4.19 mmHg for DBP, which show the superiority of the proposed model over the related works.

A Continuous Opto-Electronic Sensor for Blood Pressure Monitoring With Real-Time System

2017 ◽  
Author(s):  
Yung-Hua Kao ◽  
Paul C.-P. Chao ◽  
Chin-Long Wey
Keyword(s):  
Blood Pressure ◽  
Real Time ◽  
Blood Pressure Monitoring ◽  
Maximum Error ◽  
Infrared Light ◽  
Band Pass Filter ◽  
Time System ◽  
Real Time System ◽  
Pass Filter ◽  
Programmable Gain

A new continuous wireless opto-electronic blood pressure (BP) sensor is successfully developed by this study. The BP device introduces the principle of photoplethysmograph (PPG) to sense the change of intravascular blood volume and calculate the BP. The real-time system adopts a LEDs of red/infrared light with a wavelengths of 660 and 905 nm. The analog front-end (AFE) circuit contains a pre-amplifier, a band-pass filter, a programmable gain amplifier (PGA), a microprocessor and a wireless module. A mobile phone is also used to display continuous BPs and record statistical analysis/results for users. The passband of filter is from 0.3 to 7.2 Hz. The PGA of adjustable gain are 8 channel. As results, 10 subjects in the experimental validation, in which the obtained BPs are compared with the results from a commercial BP monitor by OMRON. The maximum error of experimental results is ± 6 mmHg, which is less than ±8 mmHg conforming to the requirement by the Advancement of Medical Instrumentation (AAMI).

scholarly journals Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

2021 ◽  
Vol 53 (2) ◽  
Author(s):  
Sen Yang ◽  
Yaping Zhang ◽  
Siu-Yeung Cho ◽  
Ricardo Correia ◽  
Stephen P. Morgan
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Deep Learning ◽  
Absolute Error ◽  
Learning Model ◽  
Physiological Measurement ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Blood Pressure Estimation ◽  
Deep Learning Model

AbstractConventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

scholarly journals Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3208 ◽  
Author(s):  
Liangju Wang ◽  
Yunhong Duan ◽  
Libo Zhang ◽  
Tanzeel U. Rehman ◽  
Dongdong Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Professional Training ◽  
Imaging System ◽  
Estimation Method ◽  
Machine Learning Algorithms ◽  
Plant Phenotyping ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Corn Plants ◽  
Hyperspectral Camera

The normalized difference vegetation index (NDVI) is widely used in remote sensing to monitor plant growth and chlorophyll levels. Usually, a multispectral camera (MSC) or hyperspectral camera (HSC) is required to obtain the near-infrared (NIR) and red bands for calculating NDVI. However, these cameras are expensive, heavy, difficult to geo-reference, and require professional training in imaging and data processing. On the other hand, the RGBN camera (NIR sensitive RGB camera, simply modified from standard RGB cameras by removing the NIR rejection filter) have also been explored to measure NDVI, but the results did not exactly match the NDVI from the MSC or HSC solutions. This study demonstrates an improved NDVI estimation method with an RGBN camera-based imaging system (Ncam) and machine learning algorithms. The Ncam consisted of an RGBN camera, a filter, and a microcontroller with a total cost of only $70 ~ 85. This new NDVI estimation solution was compared with a high-end hyperspectral camera in an experiment with corn plants under different nitrogen and water treatments. The results showed that the Ncam with two-band-pass filter achieved high performance (R2 = 0.96, RMSE = 0.0079) at estimating NDVI with the machine learning model. Additional tests showed that besides NDVI, this low-cost Ncam was also capable of predicting corn plant nitrogen contents precisely. Thus, Ncam is a potential option for MSC and HSC in plant phenotyping projects.

Adaptive Notch Filter Based Active Damping Control for Grid-Connected Inverter

2014 ◽  
Vol 986-987 ◽  
pp. 1169-1172
Author(s):  
Ping Wang ◽  
Meng Meng Cai
Keyword(s):  
Notch Filter ◽  
Active Damping ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Adaptive Notch Filter ◽  
Attenuation Characteristic ◽  
Damping Control ◽  
Grid Impedance ◽  
Grid Connected Inverter ◽  
Active Damping Control

The LCL filter is widely applied as interface between grid-connected inverter and grid due to the preferable high frequency attenuation characteristic. Under the condition of weak grid, impedance value of grid model cannot be ignored, the existence of grid impedance results in different LCL resonant frequencies, which will arise challenges of traditional active damping control. Based on the analysis of band pass filter using active damping control strategy, an adaptive active damping control is proposed in this paper by introducing the application of active notch filter, which can adjust the position of negative resonance point adaptively so as to manage sudden grid changes. Theoretical analysis and simulation results presented on the platform of grid-connected PV inverter system indicate the effectiveness and adaptability of this active damping strategy.

CMOS Implementation of Current Differencing Operational Amplifier and Its Notch Filter Application

2019 ◽  
Vol 29 (08) ◽  
pp. 2050132
Author(s):  
Muhammed Emin Başak
Keyword(s):  
Operational Amplifier ◽  
Process Model ◽  
Active Element ◽  
Harmonic Distortion ◽  
Notch Filter ◽  
Current Mode ◽  
Monte Carlo Analysis ◽  
Cmos Process ◽  
Band Pass Filter ◽  
Pass Filter

Active elements are fundamental circuits for a wide scope of scientific and industrial processes. Many researchers have examined active devices to implement filters, oscillators, rectifiers, and converters. This paper presents the current differencing operational amplifier (CDOA) as an active element, firstly implemented with CMOS transistors. The input part of this circuit is a current differencing unit and the conventional operational amplifier (Op-Amp) pursues it. A new realization of a notch filter consists of CDOA is suggested. Voltage-mode band-pass filter and current-mode notch filter are presented as a different filter applications. Simulation results using TSMC 0.18-[Formula: see text]m CMOS process model are used to verify the theoretical analyses. The sensitivity, noise, total harmonic distortion (THD) and the Monte Carlo analysis have been performed to demonstrate the effectiveness of the proposed active element and notch filter.

scholarly journals SmartScope: An AI-Powered Digital Auscultation Device To Detect Cardiopulmonary Diseases

2021 ◽  
Author(s):  
Aadhav Prabu
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Critical Factor ◽  
Raspberry Pi ◽  
Band Pass Filter ◽  
Learning Models ◽  
Pass Filter ◽  
Delayed Treatment ◽  
Learning Agent ◽  
Machine Learning Models

<p>Cardiopulmonary diseases are leading causes of death worldwide, accounting for nearly 15 million deaths annually. Accurate diagnosis and routine monitoring of these diseases by auscultation are crucial for early intervention and treatment. However, auscultation using a conventional stethoscope is low in amplitude and subjective, leading to possible missed or delayed treatment. My research aimed to develop a stethoscope called SmartScope powered by machine-learning to aid physicians in rapid analysis, confirmation, and augmentation of cardiopulmonary auscultation. Additionally, SmartScope helps patients take personalized auscultation readings at home effectively as it performs an intelligent selection of auscultation points interactively and quickly using the reinforcement learning agent: Deep Q-Network. SmartScope consists of a Raspberry Pi-enabled device, machine-learning models, and an iOS app. Users initiate the auscultation process through the app. The app communicates with the device using MQTT messaging to record the auscultation, which is augmented by an active band-pass filter and an amplifier. Additionally, the auscultation readings are refined by a Gaussian-shaped frequency filter and segmented by a Long Short-Term Memory Network. The readings are then classified using two Convolutional Recurrent Neural Networks. The results are displayed within the app and LCD. After the machine-learning models were trained, 90% accuracy for cardiopulmonary diseases was achieved, and the number of auscultation points was reduced threefold. SmartScope is an affordable, comprehensive, and user-friendly device that patients and physicians can widely use to monitor and accurately diagnose diseases like COPD, COVID-19, Asthma, and Heart Murmur instantaneously, as time is a critical factor in saving lives.</p>

Unknown Multiple Narrow-Band Disturbance Rejection in Hard Disk Drives: An Adaptive Notch Filter and Perfect Disturbance Observer Approach

2010 ◽  
Author(s):  
Xu Chen ◽  
Masayoshi Tomizuka
Keyword(s):  
Disturbance Observer ◽  
Narrow Band ◽  
Hard Disk Drives ◽  
Notch Filter ◽  
Hard Disk ◽  
Disk Drive ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Adaptive Notch Filter ◽  
Control Scheme

In this paper, an adaptive control scheme is developed to reject unknown multiple narrow-band disturbances in a hard disk drive. An adaptive notch filter is developed to efficiently estimate the frequencies of the disturbance. Based on the correctly estimated parameters, a disturbance observer with a newly designed multiple band-pass filter is constructed to achieve asymptotic perfect rejection of the disturbance. Evaluation of the control scheme is performed on a benchmark problem for HDD track following.

scholarly journals COVID-19 Prediction Applying Supervised Machine Learning Algorithms with Comparative Analysis Using WEKA

Algorithms ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 201
Author(s):  
Charlyn Nayve Villavicencio ◽  
Julio Jerison Escudero Macrohon ◽  
Xavier Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Mean Absolute Error ◽  
Learning Algorithms ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Use Of Technology

Early diagnosis is crucial to prevent the development of a disease that may cause danger to human lives. COVID-19, which is a contagious disease that has mutated into several variants, has become a global pandemic that demands to be diagnosed as soon as possible. With the use of technology, available information concerning COVID-19 increases each day, and extracting useful information from massive data can be done through data mining. In this study, authors utilized several supervised machine learning algorithms in building a model to analyze and predict the presence of COVID-19 using the COVID-19 Symptoms and Presence dataset from Kaggle. J48 Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes algorithms were applied through WEKA machine learning software. Each model’s performance was evaluated using 10-fold cross validation and compared according to major accuracy measures, correctly or incorrectly classified instances, kappa, mean absolute error, and time taken to build the model. The results show that Support Vector Machine using Pearson VII universal kernel outweighs other algorithms by attaining 98.81% accuracy and a mean absolute error of 0.012.

scholarly journals SmartScope: An AI-Powered Digital Auscultation Device To Detect Cardiopulmonary Diseases

2021 ◽  
Author(s):  
Aadhav Prabu
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Critical Factor ◽  
Raspberry Pi ◽  
Band Pass Filter ◽  
Learning Models ◽  
Pass Filter ◽  
Delayed Treatment ◽  
Learning Agent ◽  
Machine Learning Models

<p>Cardiopulmonary diseases are leading causes of death worldwide, accounting for nearly 15 million deaths annually. Accurate diagnosis and routine monitoring of these diseases by auscultation are crucial for early intervention and treatment. However, auscultation using a conventional stethoscope is low in amplitude and subjective, leading to possible missed or delayed treatment. My research aimed to develop a stethoscope called SmartScope powered by machine-learning to aid physicians in rapid analysis, confirmation, and augmentation of cardiopulmonary auscultation. Additionally, SmartScope helps patients take personalized auscultation readings at home effectively as it performs an intelligent selection of auscultation points interactively and quickly using the reinforcement learning agent: Deep Q-Network. SmartScope consists of a Raspberry Pi-enabled device, machine-learning models, and an iOS app. Users initiate the auscultation process through the app. The app communicates with the device using MQTT messaging to record the auscultation, which is augmented by an active band-pass filter and an amplifier. Additionally, the auscultation readings are refined by a Gaussian-shaped frequency filter and segmented by a Long Short-Term Memory Network. The readings are then classified using two Convolutional Recurrent Neural Networks. The results are displayed within the app and LCD. After the machine-learning models were trained, 90% accuracy for cardiopulmonary diseases was achieved, and the number of auscultation points was reduced threefold. SmartScope is an affordable, comprehensive, and user-friendly device that patients and physicians can widely use to monitor and accurately diagnose diseases like COPD, COVID-19, Asthma, and Heart Murmur instantaneously, as time is a critical factor in saving lives.</p>

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