Healthcare Technology Letters
Volume 7, Issue 5, October 2020
Volumes & issues:
Volume 7, Issue 5
October 2020
Traumatic brain injury probability of survival assessment in adults using iterative random comparison classification
- Author(s): Mohammed Salah ; Reza Saatchi ; Fiona Lecky ; Derek Burke
- Source: Healthcare Technology Letters, Volume 7, Issue 5, p. 119 –124
- DOI: 10.1049/htl.2019.0029
- Type: Article
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Trauma brain injury (TBI) is the most common cause of death and disability in young adults. A method to determine the probability of survival (Ps) in trauma called iterative random comparison classification (IRCC) was developed and its performance was evaluated in TBI. IRCC operates by iteratively comparing the test case with randomly chosen subgroups of cases from a database of known outcomes (survivors and not survivors) and determines the overall percentage match. The performance of IRCC to determine Ps in TBI was compared with two existing methods. One was Ps14 that uses regression and the other was predictive statistical diagnosis (PSD) that is based on Bayesian statistic. The TBI database contained 4124 adult cases (mean age 67.9 years, standard deviation 21.6) of which 3553 (86.2%) were survivors and 571 (13.8%) were not survivors. IRCC determined Ps for the survivors and not survivors with an accuracy of 79.0 and 71.4%, respectively, while the corresponding values for Ps14 were 97.4% (survivors) and 40.2% (not survivors) and for PSD were 90.8% (survivors) and 50% (not survivors). IRCC could be valuable for determining Ps in TBI and with a suitable database in other traumas.
Efficient implementation of LMS adaptive filter-based FECG extraction on an FPGA
- Author(s): Bhavya Vasudeva ; Puneesh Deora ; Pradhan Mohan Pradhan ; Sudeb Dasgupta
- Source: Healthcare Technology Letters, Volume 7, Issue 5, p. 125 –131
- DOI: 10.1049/htl.2020.0016
- Type: Article
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In this Letter, the field programmable gate array (FPGA) implementation of a foetal heart rate (FHR) monitoring system is presented. The system comprises a preprocessing unit to remove various types of noise, followed by a foetal electrocardiogram (FECG) extraction unit and an FHR detection unit. To improve the precision and accuracy of the arithmetic operations, a floating-point unit is developed. A least mean squares algorithm-based adaptive filter (LMS-AF) is used for FECG extraction. Two different architectures, namely series and parallel, are proposed for the LMS-AF, with the series architecture targeting lower utilisation of hardware resources, and the parallel architecture enabling less convergence time and lower power consumption. The results show that it effectively detects the R peaks in the extracted FECG with a sensitivity of 95.74–100% and a specificity of 100%. The parallel architecture shows up to an 85.88% reduction in the convergence time for non-invasive FECG databases while the series architecture shows a 27.41% reduction in the number of flip flops used when compared with the existing FPGA implementations of various FECG extraction methods. It also shows an increase of 2–7.51% in accuracy when compared to previous works.
Identification of glottal instants using electroglottographic signal for vulnerable cases of voicing
- Author(s): Tanumay Mandal ; Krothapalli Sreenivasa Rao ; Sanjay K. Gupta
- Source: Healthcare Technology Letters, Volume 7, Issue 5, p. 132 –138
- DOI: 10.1049/htl.2019.0085
- Type: Article
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Robust detection of glottal instants is essential for various speech and biomedical applications. Glottal closing and glottal opening are two crucial instants/epochs of a glottal cycle. The first-order derivative of the Electroglottographic (EGG) signal demonstrates important peaks at those locations for standard voicing, but the detection of glottal instants becomes erroneous when the peak to peak amplitude of the EGG signal is very low, irregular and unpredictable. In this work, a new efficient method is proposed for identification of glottal instants from the EGG signals including the segments of the signals where the signals are feeble with irregular periodicity. The overall accuracy of detection will be enhanced by identifying the glottal instants for the whole part of the signal including the vulnerable segments of signal. As the phase of a signal is uniform in nature, the phase information of the EGG signal has been explored to detect glottal instants accurately. Under low strength of the EGG signal, the proposed method remarkably has better performance compared to the existing instants detection methods and for pathological EGG signal, the detection accuracy of glottal instants is better than other existing methods.
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