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All Journal International Journal of Electrical and Computer Engineering JUTI: Jurnal Ilmiah Teknologi Informasi Indonesian Journal of Electrical Engineering and Computer Science
Hikmah, Nada Fitrieyatul
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Computer Science & IT Dentistry Education Electrical & Electronics Engineering Library & Information Science

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Brain tumor detection using a MobileNetV2-SSD model with modified feature pyramid network levels Hikmah, Nada Fitrieyatul; Hajjanto, Ariq Dreiki; A. Surbakti, Armand Faris; Prakosa, Nadhira Anindyafitri; Asmaria, Talitha; Sardjono, Tri Arief
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3995-4004

Abstract

Brain tumors, a subset of these malignancies, demand accurate and efficient diagnosis. Traditional methods use non-invasive medical imaging like magnetic resonance imaging (MRI) and computed tomography (CT). Although necessary for diagnosis, manual brain MRI picture segmentation is tedious and time-consuming. Using deep learning is a promising solution. This study proposes an innovative approach for brain tumor detection, focusing on meningioma tumors. Utilizing threshold-based segmentation, the MobileNetV2 architecture, a modified feature pyramid network (FPN), and single shot MultiBox detector (SSD), our model achieves precise localization and object detection. Pre-processing techniques such as grayscale conversion, histogram equalization, and Gaussian filtering enhance the MRI image quality. Morphological operations and thresholding facilitate tumor segmentation. Data augmentation and a meticulous dataset division aid in model generalization. The architecture combines MobileNetV2 as a feature extractor, SSD for object detection, and FPN for detecting small objects. Modifications, including lowering the minimum FPN level, enhance small object detection accuracy. The proposed model achieved a recall value of around 98% and a precision value of around 89%. Additionally, the proposed model achieved approximately 93% on both the dice similarity coefficient (DSC) value and the index of similarity. Based on the promising results, our research holds significant advancements for the field of medical imaging and tumor detection.
Convolutional neural network for assisting accuracy of personalized clavicle bone implant designs Mayasari, Dita Ayu; Hawari, Ihtifazhuddin; Dwiyanti, Sheba Atma; Noviyadi, Nathasya Reinelda; Andryani, Dinda Syaqila; Utomo, Muhammad Satrio; Hikmah, Nada Fitrieyatul; Asmaria, Talitha
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3208-3219

Abstract

The clavicle is a long bone that tends to be frequently fractured in the midshaft region. The plate and screw fixing method is mainly applied to address this issue. This study aims to construct a clavicle bone implant design with a consideration to achieve a high accuracy and high-quality surface between the plate and the clavicle surface. The computational tomography scanning (CT-scan) image series data were processed using a convolutional neural network (CNN) to classify the clavicle image. The CNN outcomes were gathered as three-dimensional (3D) volume data of clavicle bone. This 3D model was then proposed for the plate design. The CNN testing results of 97.4% for the image clavicle bones classification, whereas the prints of the 3D model from clavicle bone and its plate and screw design reveal compatibility between the bone surface and the plate surface. Overall, the CNN application to the series of CT images could ease the classification of clavicle bone images that would precisely construct the 3D model of clavicle bone and its suitable clavicle bone plate design. This study could contribute as a guideline for other bone plate areas that need to fit the patient’s bone geometry.
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.
Enhanced embedded system for various synthetic electrocardiogram generation using McSharry’s dynamic equation Hikmah, Nada Fitrieyatul; Setiawan, Rachmad; Andanis, Nafila Cahya; Pranata, Aldo
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1620-1631

Abstract

n electrocardiogram (ECG) is a signal that describes the heart’s electrical activity. Signal processing techniques are necessary to extract meaningful information from ECG signals. Researchers often use large databases like the PhysioNet database to evaluate the performance of algorithms. However, these databases have limitations concerning the lack of temporal or morphological variations. This study addresses this limitation by introducing a synthetic ECG capable of producing both normal 12-lead ECG signals and abnormal ECG signals and implementing it into the microcontroller. The primary contribution involves developing a synthetic ECG model using McSharry's dynamic equation model and implementing it using Mikromedia 5 for STM32F4 Capacitive as a microcontroller. This model enables users to set the desired heart rate and accurately replicates ECG waveforms using parameters ????????, ????????, and ????????, each determines the peak’s magnitude, the peak’s time duration, and the angular velocity of the trajectory. The synthetic ECG was evaluated qualitatively and quantitatively, demonstrating waveform similarity to the ECG signals. This study implies that the synthetic ECG model serves as a valuable tool for researchers and practitioners in electrocardiography. It enables the generation of normal and abnormal ECG signals, aiding in algorithm development and potentially enhancing the understanding and diagnosis of heart conditions.
DELINEATION OF ECG FEATURE EXTRACTION USING MULTIRESOLUTION ANALYSIS FRAMEWORK Hikmah, Nada Fitrieyatul; Arifin, Achmad; Sardjono, Tri Arief
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 18, No. 2, July 2020
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v18i2.a992

Abstract

ECG signals have very features time-varying morphology, distinguished as P wave, QRS complex, and T wave. Delineation in ECG signal processing is an important step used to identify critical points that mark the interval and amplitude locations in the features of each wave morphology. The results of ECG signal delineation can be used by clinicians to associate the pattern of delineation point results with morphological classes, besides delineation also produces temporal parameter values of ECG signals. The delineation process includes detecting the onset and offset of QRS complex, P and T waves that represented as pulse width, and also the detection of the peak from each wave feature. The previous study had applied bandpass filters to reduce amplitude of P and T waves, then the signal was passed through non-linear transformations such as derivatives or square to enhance QRS complex. However, the spectrum bandwidth of QRS complex from different patients or same patient may be different, so the previous method was less effective for the morphological variations in ECG signals. This study developed delineation from the ECG feature extraction based on multiresolution analysis with discrete wavelet transform. The mother wavelet used was a quadratic spline function with compact support. Finally, determination of R, T, and P wave peaks were shown by zero crossing of the wavelet transform signals, while the onset and offset were generated from modulus maxima and modulus minima. Results show the proposed method was able to detect QRS complex with sensitivity of 97.05% and precision of 95.92%, T wave detection with sensitivity of 99.79% and precision of 96.46%, P wave detection with sensitivity of 56.69% and precision of 57.78%. The implementation in real time analysis of time-varying ECG morphology will be addressed in the future research.
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.
Co-Authors A. Surbakti, Armand Faris Achmad Arifin Andanis, Nafila Cahya Andryani, Dinda Syaqila Asmaria, Talitha A’alimah, Fathin Hanum Dita Ayu Mayasari, Dita Ayu Dwi Winarsih, Meitha Auliana Dwiyanti, Sheba Atma Elparani, Sirsta Hayatu Hajjanto, Ariq Dreiki Ihtifazhuddin Hawari Noviyadi, Nathasya Reinelda Oktavia, Nabila Shafa Prakosa, Nadhira Anindyafitri PRANATA, ALDO Rifqi Hananto, R. M. Tejo Salsabila, Anadya Ghina Setiawan, Rachmad Syulthoni, Zain Budi Tri Arief Sardjono Utomo, Muhammad Satrio Wahyu Nugroho, Dwi Oktavianto