Determination of body fat percentage by electrocardiography signal with gender based artificial intelligence

Uçar, Muhammed Kürşad; Uçar, Zeliha; Uçar, Kübra; Akman, M.; Bozkurt, Mehmet Recep

doi:10.1016/j.bspc.2021.102650

Cited by 12 publications

(7 citation statements)

References 35 publications

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“…Each of the four signals has 25 features extracted. Many features have been used for PPG signals in the literature ( Uçar et al, 2021 ; Uçar et al, 2017 ). In our study, we retrieved 25 characteristics from the PPG signal.…”

Section: Methodsmentioning

confidence: 99%

A new diagnostic method for chronic obstructive pulmonary disease using the photoplethysmography signal and hybrid artificial intelligence

Melekoglu

Kocabıçak

Uçar

et al. 2022

PeerJ Computer Science

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Background and Purpose Chronic obstructive pulmonary disease (COPD), is a primary public health issue globally and in our country, which continues to increase due to poor awareness of the disease and lack of necessary preventive measures. COPD is the result of a blockage of the air sacs known as alveoli within the lungs; it is a persistent sickness that causes difficulty in breathing, cough, and shortness of breath. COPD is characterized by breathing signs and symptoms and airflow challenge because of anomalies in the airways and alveoli that occurs as the result of significant exposure to harmful particles and gases. The spirometry test (breath measurement test), used for diagnosing COPD, is creating difficulties in reaching hospitals, especially in patients with disabilities or advanced disease and in children. To facilitate the diagnostic treatment and prevent these problems, it is far evaluated that using photoplethysmography (PPG) signal in the diagnosis of COPD disease would be beneficial in order to simplify and speed up the diagnosis process and make it more convenient for monitoring. A PPG signal includes numerous components, including volumetric changes in arterial blood that are related to heart activity, fluctuations in venous blood volume that modify the PPG signal, a direct current (DC) component that shows the optical properties of the tissues, and modest energy changes in the body. PPG has typically received the usage of a pulse oximeter, which illuminates the pores and skin and measures adjustments in mild absorption. PPG occurring with every heart rate is an easy signal to measure. PPG signal is modeled by machine learning to predict COPD. Methods During the studies, the PPG signal was cleaned of noise, and a brand-new PPG signal having three low-frequency bands of the PPG was obtained. Each of the four signals extracted 25 features. An aggregate of 100 features have been extracted. Additionally, weight, height, and age were also used as characteristics. In the feature selection process, we employed the Fisher method. The intention of using this method is to improve performance. Results This improved PPG prediction models have an accuracy rate of 0.95 performance value for all individuals. Classification algorithms used in feature selection algorithm has contributed to a performance increase. Conclusion According to the findings, PPG-based COPD prediction models are suitable for usage in practice.

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Section: Methodsmentioning

confidence: 99%

A new diagnostic method for chronic obstructive pulmonary disease using the photoplethysmography signal and hybrid artificial intelligence

Melekoglu

Kocabıçak

Uçar

et al. 2022

PeerJ Computer Science

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“…Önerilen modellerin performansının test edilebilmesi için Ortalama Karakök Sapması (RMSE), Ortalama Mutlak Yüzde Hata (MAPE), Korelasyon Katsayısı 𝑅, Açıklayıcılık Katsayısı 𝑅 2 , Ortalama Mutlak Sapma (MAD) ve Ortalama Hata Karesi (MSE) kullanılmıştır [15], [16]. RMSE, MAPE, MSE, SH ve MAD değerlerinin sıfıra yakın olması 𝑅 2 ve 𝑅'nin ise bire yakın olması model performansının yüksek olduğunun göstergesidir.…”

Section: Performans Değerlendirmeunclassified

Holter Monitörü için Elektrokardiyografi Sinyali Tabanlı Yeni Bir Kan Basıncı Hesaplama Yöntemi

Uçar¹

2021

acperpro

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Amaç ve Kapsam: Hipertansiyon hastalarında tansiyonun gün içerisinde takip edilmesi ve ilaç tedavisi ile kontrol alınması hayati öneme sahiptir. Hipertansiyonun takibi sürekli kan basıncı takibi sistemleri ile yapılabilir. Ancak bu cihazların vücuda verdiği rahatsızlığı gidermek için yeni teknolojilere ihtiyaç duyulmaktadır. Bu kapsamda, çalışmanın amacı holter cihazlarına entegre edilebilir, Elektrokardiyografi (EKG) sinyali tabanlı kan basıncı ölçme yazılım sürecinin geliştirmektir. Materyal ve Metot: Çalışmada, açık kaynak paylaşımlı, beş sağlıklı bireyin üç günlük ortalama 6’şar saatlik fiziksel aktivite sırasında EKG sinyali ve kan basıncı kayıtları kullanılmıştır. Çalışmada EKG sinyali filtrelenerek 10 saniyelik epoklara ayrılmıştır. 19897 epoktan 25 adet zaman domeninde özellik çıkarılmıştır. Çıkarılan özellikler ile makine öğrenmesi tabanlı Sistolik (Büyük) ve Diastolik (Küçük) kan basıncı tahmin modelleri geliştirilmiştir. Sonuçlar: Sistolik ve diastolik kan basıncı tahmin modelleri R ve MSE performans değerleri sırasıyla 1, 0,0112 ve 1, 0,0054 olarak tespit edilmiştir. Sonuç: Elde edilen sonuçlara göre makine algoritmaları tabanlı sistolik ve diastolik kan basıncının hesaplanabileceği ve Holter gibi vücuda daha az rahatsızlık veren cihazlara yazılımsal olarak entegre edilebileceği değerlendirilmektedir.

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“…Leveraging the versatility of ECGs, researchers have utilized it to ascertain a range of critical details including age, gender [2, 3], and historical cardiac events such as Myocardial Infarction (MI) [4], alongside diagnosing cardiac arrhythmias such as Atrial Fibrillation (AFib) and Supraventricular Tachycardia (SVT) [5, 6]. Through the advancements in deep learning, research has now expanded the potential of ECG beyond traditional applications, enabling the deduction of parameters such as sleep apnea [7], ejection fraction [8], body fat percentage [9], etc. These types of approaches may pave the way for deeper insights into a patient’s cardiac health.…”

Section: Introductionmentioning

confidence: 99%

Temporal Heart Rhythm Clusters and Physiomorphic Age Mapping: A Deep Learning Approach to Cardiovascular Risk Stratification

Manimaran,

Puthusserypady,

Dominguez

et al. 2024

Preprint

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Purpose: Understanding the intricate relationships between sleep quality and cardiovascular outcomes can potentially offer new avenues in risk stratification for cardiovascular diseases (CVD). This study aimed to evaluate the significance of biological age predicted through the analysis of sleep stages and nocturnal heart rhythms as a marker for cardiovascular risk. Methods: We leveraged an unsupervised learning approach to generate time-series clusters utilizing whole-night sleep data from N=900 patients, focusing on identifying shifts and consistencies in nocturnal heart rhythms that may indicate variations in cardiac health. Following this, a deep learning model was applied to the time-series clusters to estimate the biological age of the individuals, thereby delineating potential relationships between predicted age, biological age, sleep patterns, and heart rhythms. Results: In a distinct test set of 736 individuals, the predicted age based on this experiment showcased a higher association with mortality (Hazard Ratio (HR) 2.27, p<0.05) and CVD risk (HR 3.56, p<0.001). Conversely, the age estimated through only nocturnal heart rhythms demonstrated a HR of 2.29 (p<0.05) for all-cause mortality and 3.13 (p<0.01) for CVD risk. Conclusion: Our findings underscore the high prognostic potential of sleep and electrocardiography data in predicting cardiovascular risks. The method of utilizing predicted biological age derived from sleep stages and nocturnal heart rhythms stands as a significant metric in risk stratification for CVD. Further research in this area might foster novel strategies for early interventions based on sleep quality and cardiac health markers, potentially saving numerous lives through early detection and intervention.

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Determination of body fat percentage by electrocardiography signal with gender based artificial intelligence

Cited by 12 publications

References 35 publications

A new diagnostic method for chronic obstructive pulmonary disease using the photoplethysmography signal and hybrid artificial intelligence

A new diagnostic method for chronic obstructive pulmonary disease using the photoplethysmography signal and hybrid artificial intelligence

Holter Monitörü için Elektrokardiyografi Sinyali Tabanlı Yeni Bir Kan Basıncı Hesaplama Yöntemi

Temporal Heart Rhythm Clusters and Physiomorphic Age Mapping: A Deep Learning Approach to Cardiovascular Risk Stratification

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