Jarelh Galdos

National University of Saint Augustine

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Erasmo Sulla Espinoza

National University of Saint Augustine

Jorge Rendulich

National University of Saint Augustine

Elvis Supo

National University of Saint Augustine

Nikolai Lopez Colque

National University of Saint Augustine

Angie Medina Rodirguez

National University of Saint Augustine

Angie Medina

National University of Saint Augustine

Emily Juliana Mamani Macedo

Jesús Talavera Suarez

National University of Saint Augustine

Marcelo Delgado-del-Carpio

National University of Saint Augustine

R. Huamani R.

National University of Saint Augustine

Cooperative Institutions

National University of Saint Augustine

University of San Agustin

Juan Agustín Maza University

Tuxtla Gutierrez Institute of Technology

Tecnológico Nacional de México

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COMPARISON AND EVALUATION OF LMS-DERIVED ALGORITHMS APPLIED ON ECG SIGNALS CONTAMINATED WITH MOTION ARTIFACT DURING PHYSICAL ACTIVITIES

Applied Computer Science (2024)

Jarelh Galdos Nikolai Lopez Colque Angie Medina Rodirguez Jorge Huarca Quispe Jorge Rendulich

The acquisition of ECG signals offers physicians and specialists a very important tool in the diagnosis of cardiovascular diseases. However, very often these signals are affected by noise from various sources, including noise generated by movement during physical activity. This type of noise is known as Motion Artifact (MA) which changes the waveform of the signal, leading to erroneous readings. The elimination of this noise is performed by different filtering techniques, where the adaptive filtering using the LMS (least mean squares) algorithm stands out. The objective of this article is to determine which algorithms best deal with motion artifacts, taking into account the use of instruments or wearable equipment, in different conditions of physical activity. A comparison between different algorithms derived from LMS (NLMS, PNLMS and IPNLM) used in adaptive filtering is carried out using indicators such as: Pearson's Correlation Coefficient, Signal to Noise Ratio (SNR) and Mean Squared Error (MSE) as metrics to evaluate them. For this purpose, the mHealth database was used, which contains ECG signals taken during moderate to medium intensity physical activities. The results show that filtering by IPNLMS as well as PNLMS offers an improvement both visually and in terms of SNR, Pearson, and MSE indicators.

Artifact (error)

10.35784/acs-2024-10

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PRD as an indicator proposal in the evaluation of ECG signal acquisition prototypes in real patients

IEEE ANDESCON (2022)

Elvis Supo Marcelo Delgado-del-Carpio R. Huamani R.Jarelh Galdos Jorge Rendulich

The development of ECG acquisition devices brings with it the need to evaluate their reliability; for this purpose, the signal obtained could be compared with a signal previously taken, however, signals acquired from real patients lead to errors in the comparison process due to the non-stationary and non-deterministic nature of ECG signals. Thus, PRD is proposed as an objective and quantitative evaluation method; the process of experimentation was done on signals obtained from an arrhythmia generator and real patients in the Corire-Peru area. Both sets of signals were taken with an ECG acquisition prototype based on Cortex ARM and ADS1298, as well as with a commercial ECG (CardioExpress SL6A) and then PRD was calculated for an analysis of results.

SIGNAL (programming language)

10.1109/andescon56260.2022.9989674

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Development of a Low-Cost Security System Based on Voice Recognition Using Artificial Intelligence

International Journal of Electrical and Electronics Engineering (2024)

Willians Jeremy Luna Condori Emily Juliana Mamani Macedo Alex Leon Ppacco Huamani Jesús Talavera Suarez Jarelh Galdos

Security system

10.14445/23488379/ijeee-v11i6p135

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Continuous Blood Pressure Estimation in Wearable Devices Using Photoplethysmography: A Review

International Journal of Emerging Technology and Advanced Engineering (2022)

Angie Medina Nikolai Lopez Colque Jarelh Galdos Elvis Supo Jorge Rendulich

Cardiovascular diseases (CVD) are among those with the highest mortality rates, and various wearable devices for continuous monitoring are emerging as a complement to medical procedures. Blood pressure (BP) monitoring in wearable devices, in order to be continuous, must be performed noninvasively, thus involving photoplethysmography (PPG), a technology that has been widely studied in recent years as a non-invasive solution for BP estimation. However, continuous data acquisition in a wearable system is still a challenge, one of the reasons being the noise caused by movement, the correct use of the PPG signal, and the estimation method to be used. This paper reviews the advances in blood pressure estimation based on photoplethysmography, focusing on the analysis of the preprocessing (ICA, FIR, adaptive filters) of the signals. Among the filters reviewed, the most suitable for dealing with Motion Artifacts (MA) of a wearable system are the adaptive filters, because conventional filters are limited to work only in the band for which they are designed, which does not always cover the spectrum of the MA. A review of the estimation methods is also carried out, among them machine learning stands out because it shows greater growth due to the new proposals that use more signals and obtain better results in terms of accuracy. The objective is to know and analyze the appropriate preprocessing filters and estimation methods from the perspective of wearable systems using PPG sensors affected by AM. Keywords— Blood Pressure Estimation, PAT, PTT, Machine Learning, Photoplethysmography, adaptive filtering.

Photoplethysmogram

Wearable Technology

Continuous monitoring

10.46338/ijetae1022_12

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Citations (4)