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Contactless respiration rate measurement using optical method and empirical mode decomposition

Gan KB, Yahyavi ES, Ismail MS

Technol Health Care, 2016 Sep 14;24(5):761-8.
PMID: 27163300 DOI: 10.3233/THC-161161

BACKGROUND: At the emergency triage center, assessment of the present of the danger signs and measurement of vital signs are measured according to the guidelines. The respiration rate is still posing a challenge to the doctor as it is impractical to use conventional devices. Attaching measurement devices to the patient will induce artificial measurements (self-awareness stress effects) besides being time-consuming. Currently, the medical officers visually count the number of times the chest movement in a minute, sometimes poses cultural challenges especially for female patients.
OBJECTIVE: The main objective of this paper is to develop a robust algorithm to extract respiration rate using the contactless displacement sensor.
METHODS: In this study, chest movements were used as an indicative of inspiration and expiration to measure respiratory rate using the contactless displacement sensor. The contactless optical signals were recorded from 32 healthy subjects in four different controlled breathing conditions: rest, coughing, talking and hand movement to obtain the motion artifacts that the patients may have in the emergency department. The Empirical mode decomposition (EMD) algorithm was used to derive continuous RR signal from the contactless optical signal.
RESULTS: The analysis showed that there is a good correlation (0.9702) with RMSE of 0.33 breaths per minutes between the contact respiration rate and contactless respiration rate using empirical mode decomposition method.
CONCLUSION: It can be concluded that the empirical mode decomposition method can extract the respiration rate of the contactless optical signal from chest movement.
Transabdominal fetal heart rate detection using NIR photopleythysmography: instrumentation and clinical results

Gan KB, Zahedi E, Mohd Ali MA

IEEE Trans Biomed Eng, 2009 Aug;56(8):2075-82.
PMID: 19403354 DOI: 10.1109/TBME.2009.2021578

In obstetrics, fetal heart rate (FHR) detection remains the standard for intrapartum assessment of fetal well-being. In this paper, a low-power (< 55 mW) optical technique is proposed for transabdominal FHR detection using near-infrared photoplesthysmography (PPG). A beam of IR-LED (890 nm) propagates through to the maternal abdomen and fetal tissues, resulting in a mixed signal detected by a low-noise detector situated at a distance of 4 cm. Low-noise amplification and 24-bit analog-to-digital converter resolution ensure minimum effect of quantization noise. After synchronous detection, the mixed signal is processed by an adaptive filter to extract the fetal signal, whereas the PPG from the mother's index finger is the reference input. A total of 24 datasets were acquired from six subjects at 37 +/- 2 gestational weeks. Results show a correlation coefficient of 0.96 (p-value < 0.001) between the proposed optical and ultrasound FHR, with a maximum error of 4%. Assessment of the effect of probe position on detection accuracy indicates that the probe should be close to fetal tissues, but not necessarily restricted to head or buttocks.
Secondary triage classification using an ensemble random forest technique

Azeez D, Gan KB, Mohd Ali MA, Ismail MS

Technol Health Care, 2015;23(4):419-28.
PMID: 25791174 DOI: 10.3233/THC-150907

BACKGROUND: Triage of patients in the emergency department is a complex task based on several uncertainties and ambiguous information. Triage must be implemented within two to five minutes to avoid potential fatality and increased waiting time.
OBJECTIVE: An intelligent triage system has been proposed for use in a triage environment to reduce human error.
METHODS: This system was developed based on the objective primary triage scale (OPTS) that is currently used in the Universiti Kebangsaan Malaysia Medical Center. Both primary and secondary triage models are required to develop this system. The primary triage model has been reported previously; this work focused on secondary triage modelling using an ensemble random forest technique. The randomized resampling method was proposed to balance the data unbalance prior to model development.
RESULTS: The results showed that the 300% resampling gave a low out-of-bag error of 0.02 compared to 0.37 without pre-processing. This model has a sensitivity and specificity of 0.98 and 0.89, respectively, for the unseen data.
CONCLUSION: With this combination, the random forest reduces the variance, and the randomized resembling reduces the bias, leading to the reduced out-of-bag error.
KEYWORDS: Decision support system; emergency department; random forest; randomized resampling
Fulltext The Principles of Hearable Photoplethysmography Analysis and Applications in Physiological Monitoring-A Review

Azudin K, Gan KB, Jaafar R, Ja'afar MH

Sensors (Basel), 2023 Jul 18;23(14).
PMID: 37514778 DOI: 10.3390/s23146484

Not long ago, hearables paved the way for biosensing, fitness, and healthcare monitoring. Smart earbuds today are not only producing sound but also monitoring vital signs. Reliable determination of cardiovascular and pulmonary system information can explore the use of hearables for physiological monitoring. Recent research shows that photoplethysmography (PPG) signals not only contain details on oxygen saturation level (SPO2) but also carry more physiological information including pulse rate, respiration rate, blood pressure, and arterial-related information. The analysis of the PPG signal from the ear has proven to be reliable and accurate in the research setting. (1) Background: The present integrative review explores the existing literature on an in-ear PPG signal and its application. This review aims to identify the current technology and usage of in-ear PPG and existing evidence on in-ear PPG in physiological monitoring. This review also analyzes in-ear (PPG) measurement configuration and principle, waveform characteristics, processing technology, and feature extraction characteristics. (2) Methods: We performed a comprehensive search to discover relevant in-ear PPG articles published until December 2022. The following electronic databases: Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, Scopus, Web of Science, and PubMed were utilized to conduct the studies addressing the evidence of in-ear PPG in physiological monitoring. (3) Results: Fourteen studies were identified but nine studies were finalized. Eight studies were on different principles and configurations of hearable PPG, and eight studies were on processing technology and feature extraction and its evidence in in-ear physiological monitoring. We also highlighted the limitations and challenges of using in-ear PPG in physiological monitoring. (4) Conclusions: The available evidence has revealed the future of in-ear PPG in physiological monitoring. We have also analyzed the potential limitation and challenges that in-ear PPG will face in processing the signal.
Fulltext Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department

Azeez D, Ali MA, Gan KB, Saiboon I

Springerplus, 2013;2:416.
PMID: 24052927 DOI: 10.1186/2193-1801-2-416

Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice, medical research and point of care. In this paper, a primary triage model was designed using two different methods: an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).When the patient is presented at the triage counter, the system will capture their vital signs and chief complains beside physiology stat and general appearance of the patient. This data will be managed and analyzed in the data server and the patient's emergency status will be reported immediately. The proposed method will help to reduce the queue time at the triage counter and the emergency physician's burden especially duringdisease outbreak and serious disaster. The models have been built with 2223 data set extracted from the Emergency Department of the Universiti Kebangsaan Malaysia Medical Centre to predict the primary triage category. Multilayer feed forward with one hidden layer having 12 neurons has been used for the ANN architecture. Fuzzy subtractive clustering has been used to find the fuzzy rules for the ANFIS model. The results showed that the RMSE, %RME and the accuracy which evaluated by measuring specificity and sensitivity for binary classificationof the training data were 0.14, 5.7 and 99 respectively for the ANN model and 0.85, 32.00 and 96.00 respectively for the ANFIS model. As for unseen data the root mean square error, percentage the root mean square error and the accuracy for ANN is 0.18, 7.16 and 96.7 respectively, 1.30, 49.84 and 94 respectively for ANFIS model. The ANN model was performed better for both training and unseen data than ANFIS model in term of generalization. It was therefore chosen as the technique to develop the primary triage prediction model. This primary triage model will be combined with the secondary triage prediction model to produce the final triage category as a tool to assist the medical officer in the emergency department.
Analysis of myocardial infarction signals using optical technique

Mahri N, Gan KB, Mohd Ali MA, Jaafar MH, Meswari R

J Med Eng Technol, 2016 May;40(4):155-61.
PMID: 27010162 DOI: 10.3109/03091902.2016.1153740

The risk of heart attack or myocardial infarction (MI) may lead to serious consequences in mortality and morbidity. Current MI management in the triage includes non-invasive heart monitoring using an electrocardiogram (ECG) and the cardic biomarker test. This study is designed to explore the potential of photoplethysmography (PPG) as a simple non-invasive device as an alternative method to screen the MI subjects. This study emphasises the usage of second derivative photoplethysmography (SDPPG) intervals as the extracted features to classify the MI subjects. The statistical analysis shows the potential of "a-c" interval and the corrected "a-cC" interval to classify the subject. The sensitivity of the predicted model using "a-c" and "a-cC" is 90.6% and 81.2% and the specificity is 87.5% and 84.4%, respectively.
Utilization of second derivative photoplethysmographic features for myocardial infarction classification

Mahri N, Gan KB, Meswari R, Jaafar MH, Mohd Ali MA

J Med Eng Technol, 2017 May;41(4):298-308.
PMID: 28351231 DOI: 10.1080/03091902.2017.1299229

Myocardial infarction (MI) is a common disease that causes morbidity and mortality. The current tools for diagnosing this disease are improving, but still have some limitations. This study utilised the second derivative of photoplethysmography (SDPPG) features to distinguish MI patients from healthy control subjects. The features include amplitude-derived SDPPG features (pulse height, ratio, jerk) and interval-derived SDPPG features (intervals and relative crest time (RCT)). We evaluated 32 MI patients at Pusat Perubatan Universiti Kebangsaan Malaysia and 32 control subjects (all ages 37-87 years). Statistical analysis revealed that the mean amplitude-derived SDPPG features were higher in MI patients than in control subjects. In contrast, the mean interval-derived SDPPG features were lower in MI patients than in the controls. The classifier model of binary logistic regression (Model 7), showed that the combination of SDPPG features that include the pulse height (d-wave), the intervals of "ab", "ad", "bc", "bd", and "be", and the RCT of "ad/aa" could be used to classify MI patients with 90.6% accuracy, 93.9% sensitivity and 87.5% specificity at a cut-off value of 0.5 compared with the single features model.
Electrochemical genosensor based on RNA-responsive human telomeric G-quadruplex DNA: A proof-of-concept with SARS-CoV-2 RNA

Ibrahim N, Gan KB, Mohd Yusof NY, Goh CT, Krupa B N, Tan LL

Talanta, 2024 Mar 18;274:125916.
PMID: 38547835 DOI: 10.1016/j.talanta.2024.125916

In this report, a facile and label-free electrochemical RNA biosensor is developed by exploiting methylene blue (MB) as an electroactive positive ligand of G-quadruplex. The electrochemical response mechanism of the nucleic acid assay was based on the change in differential pulse voltammetry (DPV) signal of adsorbed MB on the immobilized human telomeric G-quadruplex DNA with a loop that is complementary to the target RNA. Hybridization between synthetic positive control RNA and G-quadruplex DNA probe on the transducer platform rendered a conformational change of G-quadruplex to double-stranded DNA (dsDNA), and increased the redox current of cationic MB π planar ligand at the sensing interface, thereby the electrochemical signal of the MB-adsorbed duplex is proportional to the concentration of target RNA, with SARS-CoV-2 (COVID-19) RNA as the model. Under optimal conditions, the target RNA can be detected in a linear range from 1 zM to 1 μM with a limit of detection (LOD) obtained at 0.59 zM for synthetic target RNA and as low as 1.4 copy number for positive control plasmid. This genosensor exhibited high selectivity towards SARS-CoV-2 RNA over other RNA nucleotides, such as SARS-CoV and MERS-CoV. The electrochemical RNA biosensor showed DPV signal, which was proportional to the 2019-nCoV_N_positive control plasmid from 2 to 200000 copies (R2 = 0.978). A good correlation between the genosensor and qRT-PCR gold standard was attained for the detection of SARS-CoV-2 RNA in terms of viral copy number in clinical samples from upper respiratory specimens.
Fulltext Automated Gait Analysis Based on a Marker-Free Pose Estimation Model

Hii CST, Gan KB, Zainal N, Mohamed Ibrahim N, Azmin S, Mat Desa SH, et al.

Sensors (Basel), 2023 Jul 18;23(14).
PMID: 37514783 DOI: 10.3390/s23146489

Gait analysis is an essential tool for detecting biomechanical irregularities, designing personalized rehabilitation plans, and enhancing athletic performance. Currently, gait assessment depends on either visual observation, which lacks consistency between raters and requires clinical expertise, or instrumented evaluation, which is costly, invasive, time-consuming, and requires specialized equipment and trained personnel. Markerless gait analysis using 2D pose estimation techniques has emerged as a potential solution, but it still requires significant computational resources and human involvement, making it challenging to use. This research proposes an automated method for temporal gait analysis that employs the MediaPipe Pose, a low-computational-resource pose estimation model. The study validated this approach against the Vicon motion capture system to evaluate its reliability. The findings reveal that this approach demonstrates good (ICC(2,1) > 0.75) to excellent (ICC(2,1) > 0.90) agreement in all temporal gait parameters except for double support time (right leg switched to left leg) and swing time (right), which only exhibit a moderate (ICC(2,1) > 0.50) agreement. Additionally, this approach produces temporal gait parameters with low mean absolute error. It will be useful in monitoring changes in gait and evaluating the effectiveness of interventions such as rehabilitation or training programs in the community.
Fulltext Comparison of sPLA2IIA performance with high-sensitive CRP neutrophil percentage PCT and lactate to identify bacterial infection

Tan TL, Kang CW, Ooi KS, Tan ST, Ahmad NS, Nasuruddin DN, et al.

Sci Rep, 2021 05 31;11(1):11369.
PMID: 34059757 DOI: 10.1038/s41598-021-90894-0

Early bacterial infection (BI) identification in resource-limiting Emergency Departments (ED) is challenging, especially in low- and middle-income counties (LMIC). Misdiagnosis predisposes to antibiotic overuse and propagates antimicrobial resistance. This study evaluates new emerging biomarkers, secretory phospholipase A2 group IIA (sPLA2-IIA) and compares with other biomarkers on their performance characteristic of BI detection in Malaysia, an LMIC. A prospective cohort study was conducted involving 151 consecutive patients admitted to the ED. A single measurement was taken upon patient arrival in ED and was analysed for serum levels of sPLA2-IIA, high-sensitive C-reactive protein (CRP), procalcitonin (PCT), neutrophil percentage (N%), and lactate. All biomarkers' performance was compared for the outcomes using area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. The performance of sPLA2-IIA (AUROC 0.93 [95% CI: 0.89-0.97]; Sn 80% [95% CI: 72-87]; Sp 94% [95% CI: 81-89]) was the highest among all. It was comparable with high-sensitive CRP (AUROC 0.93 [95% CI: 0.88-0.97]; Sn 75% [95% CI: 66-83]; Sp 91 [95% CI: 77-98]) but had a higher Sn and Sp. The sPLA2-IIA was also found superior to N%, PCT, and lactate. This finding suggested sPLA2-IIA was recommended biomarkers for BI detection in LMIC.

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