Displaying all 7 publications

  1. Lim PK, Ng SC, Lovell NH, Yu YP, Tan MP, McCombie D, et al.
    Physiol Meas, 2018 10 11;39(10):105005.
    PMID: 30183675 DOI: 10.1088/1361-6579/aadf1e
    OBJECTIVE: The photoplethysmography (PPG) signal, commonly used in the healthcare settings, is easily affected by movement artefact leading to errors in the extracted heart rate and SpO2 estimates. This study aims to develop an online artefact detection system based on adaptive (dynamic) template matching, suitable for continuous PPG monitoring during daily living activities or in the intensive care units (ICUs).

    APPROACH: Several master templates are initially generated by applying principal component analysis to data obtained from the PhysioNet MIMIC II database. The master template is then updated with each incoming clean PPG pulse. The correlation coefficient is used to classify the PPG pulse into either good or bad quality categories. The performance of our algorithm was evaluated using data obtained from two different sources: (i) our own data collected from 19 healthy subjects using the wearable Sotera Visi Mobile system (Sotera Wireless Inc.) as they performed various movement types; and (ii) ICU data provided by the PhysioNet MIMIC II database. The developed algorithm was evaluated against a manually annotated 'gold standard' (GS).

    MAIN RESULTS: Our algorithm achieved an overall accuracy of 91.5%  ±  2.9%, with a sensitivity of 94.1%  ±  2.7% and a specificity of 89.7%  ±  5.1%, when tested on our own data. When applying the algorithm to data from the PhysioNet MIMIC II database, it achieved an accuracy of 98.0%, with a sensitivity and specificity of 99.0% and 96.1%, respectively.

    SIGNIFICANCE: The proposed method is simple and robust against individual variations in the PPG characteristics, thus making it suitable for a diverse range of datasets. Integration of the proposed artefact detection technique into remote monitoring devices could enhance reliability of the PPG-derived physiological parameters.

  2. Shariati NH, Zahedi E, Jajai HM
    Physiol Meas, 2008 Mar;29(3):365-74.
    PMID: 18367811 DOI: 10.1088/0967-3334/29/3/007
    Bilateral PPG signals have been used for comparative study of two groups of healthy (free from any cardiovascular risk factors) and diabetic (as cardiovascular disease risk group) subjects in the age-matched range 40-50 years. The peripheral blood pulsations were recorded simultaneously from right and left index fingers for 90 s. Pulses have been modeled with the ARX440 model in the interval of 300 sample points with 100 sample points overlap between segments. Model parameters of three segments based on the highest fitness (higher than 80%) of modeled segments were retained for each subject. Subsequently, principal component analysis (PCA) was applied to the parameters of retained segments to eliminate the existing correlation among parameters and provide uncorrelated variables. The first principal component (contains 78.2% variance of data) was significantly greater in diabetic than in control groups (P < 0.0001, 0.74 +/- 2.01 versus -0.53 +/- 1.66). In addition the seventh principal component, which contains 0.02% of the data variance, was significantly lower in diabetic than in control groups (P < 0.05, -0.007 +/- 0.03 versus 0.005 +/- 0.03). Finally, linear discrimination analysis (LDA) was used to classify the subjects. The classification was done using the robust leaving-one-subject-out method. LDA could classify the subjects with 71.7% sensitivity and 70.2% specificity while the male subjects resulted in a highly acceptable result for the sensitivity (81%). The present study showed that PPG signals can be used for vascular function assessment and may find further application for detection of vascular changes before onset of clinical diseases.
  3. Ibrahim F, Ismail NA, Taib MN, Wan Abas WA
    Physiol Meas, 2004 Jun;25(3):607-15.
    PMID: 15253113 DOI: 10.1088/0967-3334/25/3/002
    This paper describes a model for predicting hemoglobin (Hb) by using bioelectrical impedance analysis (BIA) in dengue patients in the Hospital Universiti Kebangsaan Malaysia (HUKM). Bioelectrical impedance measurements were conducted on 83 (47 males and 36 females) serologically confirmed dengue fever (DF) and dengue hemorrhagic fever (DHF) patients during their hospitalization. The predictive equation for Hb was derived using multivariate analysis. We investigated all the parameters in BIA, patients' symptom and demographic data. In this developed model, four predictors (reactance (XC), sex, weight and vomiting) were found to be the best predictive factors for modeling Hb in dengue patients. However, the model can only explain approximately 42% of the variation in Hb status, thus single frequency bio-impedance stand-alone technique is insufficient to monitor Hb for the DF and DHF patients. Further investigation using multi-frequency BIA is recommended in modeling Hb to achieve the most parsimonious model.
  4. Bakar AA, Lim YL, Wilson SJ, Fuentes M, Bertling K, Taimre T, et al.
    Physiol Meas, 2013 Feb;34(2):281-9.
    PMID: 23363933 DOI: 10.1088/0967-3334/34/2/281
    Optical sensing offers an attractive option for detection of surface biopotentials in human subjects where electromagnetically noisy environments exist or safety requirements dictate a high degree of galvanic isolation. Such circumstances may be found in modern magnetic resonance imaging systems for example. The low signal amplitude and high source impedance of typical biopotentials have made optical transduction an uncommon sensing approach. We propose a solution consisting of an electro-optic phase modulator as a transducer, coupled to a vertical-cavity surface-emitting laser and the self-mixing signal detected via a photodiode. This configuration is physically evaluated with respect to synthesized surface electrocardiographic (EKG) signals of varying amplitudes and using differing optical feedback regimes. Optically detected EKG signals using strong optical feedback show the feasibility of this approach and indicate directions for optimization of the electro-optic transducer for improved signal-to-noise ratios. This may provide a new means of biopotential detection suited for environments characterized by harsh electromagnetic interference.
  5. Khorshidtalab A, Salami MJ, Hamedi M
    Physiol Meas, 2013 Nov;34(11):1563-79.
    PMID: 24152422 DOI: 10.1088/0967-3334/34/11/1563
    The tradeoff between computational complexity and speed, in addition to growing demands for real-time BMI (brain-machine interface) systems, expose the necessity of applying methods with least possible complexity. Willison amplitude (WAMP) and slope sign change (SSC) are two promising time-domain features only if the right threshold value is defined for them. To overcome the drawback of going through trial and error for the determination of a suitable threshold value, modified WAMP and modified SSC are proposed in this paper. Besides, a comprehensive assessment of statistical time-domain features in which their effectiveness is evaluated with a support vector machine (SVM) is presented. To ensure the accuracy of the results obtained by the SVM, the performance of each feature is reassessed with supervised fuzzy C-means. The general assessment shows that every subject had at least one of his performances near or greater than 80%. The obtained results prove that for BMI applications, in which a few errors can be tolerated, these combinations of feature-classifier are suitable. Moreover, features that could perform satisfactorily were selected for feature combination. Combinations of the selected features are evaluated with the SVM, and they could significantly improve the results, in some cases, up to full accuracy.
  6. Khare SK, Bajaj V, Acharya UR
    Physiol Meas, 2023 Mar 08;44(3).
    PMID: 36787641 DOI: 10.1088/1361-6579/acbc06
    Objective.Schizophrenia (SZ) is a severe chronic illness characterized by delusions, cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking. Timely detection and treatment of SZ are necessary to avoid long-term consequences. Electroencephalogram (EEG) signals are one form of a biomarker that can reveal hidden changes in the brain during SZ. However, the EEG signals are non-stationary in nature with low amplitude. Therefore, extracting the hidden information from the EEG signals is challenging.Approach.The time-frequency domain is crucial for the automatic detection of SZ. Therefore, this paper presents the SchizoNET model combining the Margenau-Hill time-frequency distribution (MH-TFD) and convolutional neural network (CNN). The instantaneous information of EEG signals is captured in the time-frequency domain using MH-TFD. The time-frequency amplitude is converted to two-dimensional plots and fed to the developed CNN model.Results.The SchizoNET model is developed using three different validation techniques, including holdout, five-fold cross-validation, and ten-fold cross-validation techniques using three separate public SZ datasets (Dataset 1, 2, and 3). The proposed model achieved an accuracy of 97.4%, 99.74%, and 96.35% on Dataset 1 (adolescents: 45 SZ and 39 HC subjects), Dataset 2 (adults: 14 SZ and 14 HC subjects), and Dataset 3 (adults: 49 SZ and 32 HC subjects), respectively. We have also evaluated six performance parameters and the area under the curve to evaluate the performance of our developed model.Significance.The SchizoNET is robust, effective, and accurate, as it performed better than the state-of-the-art techniques. To the best of our knowledge, this is the first work to explore three publicly available EEG datasets for the automated detection of SZ. Our SchizoNET model can help neurologists detect the SZ in various scenarios.
  7. Krupa BN, Mohd Ali MA, Zahedi E
    Physiol Meas, 2009 Aug;30(8):729-43.
    PMID: 19550027 DOI: 10.1088/0967-3334/30/8/001
    Cardiotocograph (CTG) is widely used in everyday clinical practice for fetal surveillance, where it is used to record fetal heart rate (FHR) and uterine activity (UA). These two biosignals can be used for antepartum and intrapartum fetal monitoring and are, in fact, nonlinear and non-stationary. CTG recordings are often corrupted by artifacts such as missing beats in FHR, high-frequency noise in FHR and UA signals. In this paper, an empirical mode decomposition (EMD) method is applied on CTG signals. A recursive algorithm is first utilized to eliminate missing beats. High-frequency noise is reduced using EMD followed by the partial reconstruction (PAR) method, where the noise order is identified by a statistical method. The obtained signal enhancement from the proposed method is validated by comparing the resulting traces with the output obtained by applying classical signal processing methods such as Butterworth low-pass filtering, linear interpolation and a moving average filter on 12 CTG signals. Three obstetricians evaluated all 12 sets of traces and rated the proposed method, on average, 3.8 out of 5 on a scale of 1(lowest) to 5 (highest).
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