Hikmah, Nada Fitrieyatul
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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Developing Bluetooth phonocardiogram for detecting heart murmurs using hybrid MFCC and LSTM Wahyu Nugroho, Dwi Oktavianto; Hikmah, Nada Fitrieyatul; A’alimah, Fathin Hanum; Oktavia, Nabila Shafa; Dwi Winarsih, Meitha Auliana; Elparani, Sirsta Hayatu; Rifqi Hananto, R. M. Tejo
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp878-887

Abstract

Cardiovascular disease is a leading global cause of mortality. Most stethoscopes still necessitate the use of tubing, which entails direct physical contact between the healthcare provider and patient. The stethoscope can serve as a means of transmission if it is utilized on individuals who have been diagnosed with airborne and droplet-borne infectious illnesses. A prototype was created to capture heart sounds using a Phonocardiography (PCG) device over website-based Bluetooth connectivity. This approach offers the benefits of being cost-effective, facilitating computer-aided diagnostics, and being wearable. In addition, the primary significance of this study resides in the identification of heart sound irregularities caused by cardio dynamic abnormalities of the heart valves, known as murmurs. The heart sound categorization process utilizes a machine learning model that involves extracting 25 Mel frequency cepstral coefficients (MFCC) as features. The model employs a hybrid approach combining convolutional neural network and long short-term memory (CNN-LSTM) techniques. The research findings indicate that the suggested model achieves an average accuracy rate of 95.9% over five distinct categories, i.e., normal, atrial stenosis, mitral regurgitation, mitral stenosis, and mitral valves prolapse. Further study can be conducted on hardware development by incorporating an infrared sensor at the fingertip of the stethoscope.
Non-contact breathing rate monitoring using infrared thermography and machine learning Salsabila, Anadya Ghina; Setiawan, Rachmad; Hikmah, Nada Fitrieyatul; Syulthoni, Zain Budi
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp669-680

Abstract

Monitoring vital physiological parameters such as breathing rate (BR) is crucial for assessing patient health. However, current contact-based measurement methods often cause discomfort, particularly in infants or burn patients. This study aims to develop a non-contact system for monitoring BR using infrared thermography (IRT). This approach permits to detects and tracks the nose from thermal video, extracts temperature variations into a breathing signal, and processes this signal to estimate BR. The estimated BR is then classified into three health categories (bradypnea/normal/tachypnea) using k-nearest neighbors (k-NN). To evaluate system accuracy and robustness, experiments were conducted under three conditions: (i) stationary breathing, (ii) breathing with head movements, and (iii) specific breathing patterns. Results demonstrated high consistency with contact-based photoplethysmography (PPG) measurements, achieving complement of the absolute normalized difference (CAND) index values of 94.57%, 93.71%, and 96.06% across the three conditions and mean absolute BR errors of 1.045 bpm, 1.259 bpm, and 0.607 bpm. The k-NN classifier demonstrated high performance with training, validation, and testing accuracies of 100%, 100%, and 99.2%, respectively. Sensitivity, specificity, precision, and F-measure results confirm system reliability for non-contact BR monitoring in clinical and practical settings.