cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Triwiyanto
Contact Email
triwiyanto123@gmail.com
Phone
+628155126883
Journal Mail Official
editorial.jeeemi@gmail.com
Editorial Address
Department of Electromedical Engineering, Poltekkes Kemenkes Surabaya Jl. Pucang Jajar Timur No. 10, Surabaya, Indonesia
Location
Kota surabaya,
Jawa timur
INDONESIA
Journal of Electronics, Electromedical Engineering, and Medical Informatics
Published by Polilteknik Kesehatan Kemenkes Surabaya
ISSN : -     EISSN : 26568632     DOI : https://doi.org/10.35882/jeeemi
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
The Journal of Electronics, Electromedical Engineering, and Medical Informatics (JEEEMI) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics which covers three (3) majors areas of research that includes 1) Electronics, 2) Biomedical Engineering, and 3)Medical Informatics (emphasize on hardware and software design). Submitted papers must be written in English for an initial review stage by editors and further review process by a minimum of two reviewers.
Arjuna Subject : Teknik (semua kategori) - Teknik Biomedis
Articles 343 Documents
 31   32   33   34   35 
Structured Nursing Handover Report Generation from Clinical Speech using Fine-Tuned XLSR-53 and T5: A Benchmarking Study Sasikala D; Siva Sathya S; Niranjan Kumar D; Vignesh S
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 4 (2026): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i4.1778

Abstract

Accurate nursing handovers are critical for patient safety, as miscommunication during shift transitions leads to irreversible clinical errors. This work proposed an end-to-end pipeline that converts unstructured clinical nursing speech into standardized handover reports using a fine-tuned XLSR-53 acoustic model and T5-base text-to-text transformer. An Australian English clinical corpus of 200 synthetic nursing handover recordings from the CSIRO data access portal was utilised in this work. This benchmarking study was conducted within the CSIRO synthetic Australian English nursing handover corpus and does not represent a broad cross-domain clinical ASR benchmark. A domain-specific benchmarking study across seven state-of-the-art ASR architectures (Whisper Tiny/Base/Small, Wav2Vec2 Base/Large, HuBERT Large, XLSR-53) was conducted using this corpus. The experimental results further revealed XLSR-53 as the optimal architecture for clinical nursing speech recognition. A partial layer-freeze strategy was adopted in XLSR-53 by freezing the first 12 of 24 encoder layers, empirically validated through an ablation study with five freeze configurations (L=0, 6, 12, 18, 24). XLSR-53 preserves cross-lingual phonetic representations while enabling clinical vocabulary adaptation. A clinically motivated evaluation framework using curated medical vocabulary terms computes Medical Precision, Recall, and F1-Score along with standard WER, CER, and PER to assess reliability in clinical term recognition. Benchmarking against Google Health AI's MedASR zero-shot revealed that the proposed system XLSR-53 (L=12) achieved 17.15% WER against MedASR's 28.87% (p<0.001) with a Medical F1-Score of 0.98 and ROUGE-L of 0.92. Although results were obtained on synthetic Australian English speech, performance under real clinical conditions with background noise, overlapping speakers, and spontaneous interruptions requires further validation.
A Multimodal Graph Neural Network for Multiclass ADHD and ASD Classification with Leakage-Aware Evaluation Chofifatul Hidayah; Wiharto Wiharto; Esti Suryani
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 4 (2026): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i4.1801

Abstract

Neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) share overlapping clinical symptoms, complicating diagnosis and motivating objective, data-driven approaches using neuroimaging and machine learning. Graph neural networks (GNNs) have shown strong performance in this domain, yet many existing studies rely on single-modality data, transductive learning, and feature selection procedures that may introduce information leakage and inflate reported accuracy. This study proposes a multimodal graph learning framework that integrates resting-state fMRI (rs-fMRI), structural MRI (sMRI), and demographic data for classifying ADHD, ASD, and healthy controls (HC). The framework integrates temporal stability-based functional connectivity, hybrid feature selection, and adaptive multi-graph learning to exploit complementary information across these modalities. Using the ADHD-200 and ABIDE datasets, the framework is evaluated under three protocols that progressively tighten control over information leakage: transductive learning with global feature selection, inductive learning with global feature selection, and inductive learning with fold-wise feature selection. Results show that classification performance is highest under the transductive, globally-selected setting (85.5% accuracy for HC vs ADHD vs ASD, 92.5% for HC vs ASD, and 90.4% for HC vs ADHD), but decreases under the strictest leakage-aware protocol (70.9%, 81.7%, and 79.3%, respectively). This performance gap indicates that conventional evaluation protocols can substantially overestimate real-world generalization. Importantly, the proposed framework still achieves reasonable accuracy under the strictest setting, suggesting genuine discriminative capability beyond evaluation artifacts. These findings emphasize that leakage-aware evaluation, although yielding lower numbers, provides a more realistic and trustworthy estimate of model performance, highlighting its importance for developing reliable neuroimaging-based GNN models
Electrooculography-Based Voluntary Eye-Blink Detection for Arabic Assistive Communication Afrah Thamer; Mussab Alaziz
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 4 (2026): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i4.1848

Abstract

People with profound neuromotor disabilities commonly experience significant speech disability and lack of voluntary control over their limbs, retaining, however, the ability to perform deliberate eyelid blinks. Such retained capability presents an opportunity to be used as assistive communication. Nevertheless, reliable detection of voluntary blinks and decoding of communication patterns based on them is challenged by variation in the waveforms, amplitudes, durations, and inter-blink intervals of the voluntary eye blinks. In this work, a cost-efficient vertical electrooculography (EOG) approach for detecting voluntary eye blinks and decoding predefined assistive messages in Arabic was presented and assessed. The pipeline consists of signal conditioning, root-mean-square envelope calculation, adaptive hysteresis-based thresholding, temporal segmentation, and rule-based interpretation of single- and double-blink patterns. Quiet-background threshold updating and locking it during the communication window period were used to increase the robustness of the pattern detection process, while autocorrelation analysis and temporal constraints were utilized to interpret patterns. A single blink is associated with binary 0, and a double blink with binary 1; four consecutive pattern positions form a 4-bit frame that represents one of sixteen predefined Arabic messages. Unclear patterns are excluded from processing and not mapped into any valid message. The proposed approach was evaluated offline on 4,000 word-level recordings from 25 healthy participants, with verification using videos. The F1-score of 95.69%, macro-F1-score of 98.30%, and message-decoding accuracy of 92.98% were obtained. These findings demonstrate the possibility of implementing an interpretable and cost-efficient vertical-EOG approach for message-level Arabic assistive communication without morse-code-type encoding and letter-by-letter spelling. Direct mapping of patterns into messages can help reduce communication effort and time. Nevertheless, since the present evaluation was conducted offline and included only healthy subjects, further validation with target users and real-time implementation should be conducted before deployment.

Page 35 of 35 | Total Record : 343

 31   32   33   34   35 

Filter by Year

2019 2026


Reset
Filter By Issues
All Issue Vol 8 No 4 (2026): October Vol 8 No 3 (2026): July Vol 8 No 2 (2026): April Vol 8 No 1 (2026): January Vol 7 No 4 (2025): October Vol 7 No 3 (2025): July Vol 7 No 2 (2025): April Vol 7 No 1 (2025): January Vol 6 No 4 (2024): October Vol 6 No 3 (2024): July Vol 6 No 2 (2024): April Vol 6 No 1 (2024): January Vol 5 No 4 (2023): October Vol 5 No 3 (2023): July Vol 5 No 2 (2023): April Vol 5 No 1 (2023): January Vol 4 No 4 (2022): October Vol 4 No 3 (2022): July Vol 4 No 2 (2022): April Vol 4 No 1 (2022): January Vol 3 No 3 (2021): October Vol 3 No 2 (2021): July Vol 3 No 1 (2021): January Vol 2 No 3 (2020): October Vol 2 No 2 (2020): July Vol 2 No 1 (2020): January Vol 1 No 2 (2019): October Vol 1 No 1 (2019): July More Issue