Feature Selection for Heart Rate Variability Based Biometric Recognition Using Genetic Algorithm

Akhter, Nasreen; Dabhade, Siddharth B.; Bansod, Nagsen S.; Kale, K. V.

doi:10.1007/978-3-319-23036-8_8

Cited by 8 publications

(7 citation statements)

References 18 publications

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“…GA tries to find the optimal solution (usually the global minimum) of the function to be studied. The main advantage of this algorithm is its great ability to work with a large number of variables [55,56]. The objective of this algorithm is to optimize a series of parameters (called genes) that will then be concatenated with each other, when necessary, to provide the best results (called chromosomes).…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Martinez-Vega

Leon

Fabelo

et al. 2019

Sensors

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Hyperspectral imaging (HSI) is a non-ionizing and non-contact imaging technique capable of obtaining more information than conventional RGB (red green blue) imaging. In the medical field, HSI has commonly been investigated due to its great potential for diagnostic and surgical guidance purposes. However, the large amount of information provided by HSI normally contains redundant or non-relevant information, and it is extremely important to identify the most relevant wavelengths for a certain application in order to improve the accuracy of the predictions and reduce the execution time of the classification algorithm. Additionally, some wavelengths can contain noise and removing such bands can improve the classification stage. The work presented in this paper aims to identify such relevant spectral ranges in the visual-and-near-infrared (VNIR) region for an accurate detection of brain cancer using in vivo hyperspectral images. A methodology based on optimization algorithms has been proposed for this task, identifying the relevant wavelengths to achieve the best accuracy in the classification results obtained by a supervised classifier (support vector machines), and employing the lowest possible number of spectral bands. The results demonstrate that the proposed methodology based on the genetic algorithm optimization slightly improves the accuracy of the tumor identification in~5%, using only 48 bands, with respect to the reference results obtained with 128 bands, offering the possibility of developing customized acquisition sensors that could provide real-time HS imaging. The most relevant spectral ranges found comprise between 440. 5-465.96 nm, 498.71-509.62 nm, 556.91-575.1 nm, 593.29-615.12 nm, 636.94-666.05 nm, 698.79-731.53 nm and 884.32-902.51 nm.

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Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Martinez-Vega

Leon

Fabelo

et al. 2019

Sensors

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“…For the current work, the fitness function (FitFunc) was computed using kNN-based classification error with k = 3 [47,48,49] which is defined as:…”

Section: Feature Selectionmentioning

confidence: 99%

“…GA operator's optimization was carried out to detect the prime value of initial population, crossover and mutation. Table 1 indicates the tested values extracted via references from studies carried out previously [47,49,51]. The outcomes of chosen features integrating with tested parameters were recorded and chromosomes with the optimum outcome were utilized in the next stage.…”

Section: Feature Selectionmentioning

confidence: 99%

“…The stopping conditions utilized are Maximum Number of Generations (MaxNG) and Stall Generation Limit (SGLim). Both were set at 100 and 10, respectively [49]. If the mean alteration in the fitness gain between the chromosomes over SGLim generations was below than or equivalent to 0.000001 which is the value of Tolerances Function (TolFun), then the GA will be dismissed.…”

Section: New Generation= Number (Elite Kids) + Number (Crossover Kids)… + Number (Mutation Kids) (7)mentioning

confidence: 99%

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Stress Response Index for Traumatic Childhood Experience Based on the Fusion of Hypothalamus Pituitary Adrenocorticol and Autonomic Nervous System Biomarkers

Aimie-Salleh¹,

Malarvili²,

Whitttaker³

2020

Adv. sci. technol. eng. syst. j.

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Stress occurring in the early days of an individual was often assumed to cause several health consequences. A number of reports indicated that having to deal with unfavourable events or distress situation at a young age could tweak stress responses leading to a broad spectrum of poor mental and physical health condition. Therefore, changes identified within stress response were recommended to be taken as a measure in regulating and managing such health situation. This study combines the biomarker that represents both autonomic nervous system (ANS) and hypothalamic-pituitary-adrenocorticol (HPA) as a single measure to classify the stress response based on traumatic childhood experience and propose a stress response index as a future health indicator. Electrocardiograph (ECG), blood pressure, pulse rate and salivary cortisol (SCort) were collected from 12 participants who had traumatic childhood experience while the remaining 11 acted as the control group. The recording session was done during a Paced Auditory Serial Addition Test (PASAT). HRV was then computed from the ECG and the HRV features were extracted. Next, the best HRV features were selected using Genetic Algorithm (GA). Biomarkers such as BP, PR and SCort were then integrated with 12 HRV features picked from GA. The integrations were conducted using two fusion methods which are Euclidean distance and serial fusion. The differences in reaction of the fused features were then identified. Based on the result, the Euclidean distance (ed) which is the fused feature by the parallel fusion, displayed the most efficient reaction with accuracy, sensitivity, and specificity at 80. 0%, 83.3% and 78.3%, respectively. Support Vector Machine (SVM) was utilized to attain such result. The fused feature performance was then fed into SVM which produced indexes on stress responses. The result retrieved from these indexes acts as a measure in handling future health deliverability and perhaps could eventually enhance the health care platform for midlife individuals.

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“…For the current work, the fitness function (FitFunc) was computed using kNN-based classification error with k = 3 [89][90]7] which is defined as: (4) where α denotes kNN-based classification error and N f is cardinality of the selected features. Table 2 summarizes the parameters used in the GA along with their selected values.…”

Section: Hrv Feature Selectionmentioning

confidence: 99%

Fusion of heart rate variability and salivary cortisol for stress response identification based on adverse childhood experience

Aimie-Salleh

Malarvili

Whittaker

2019

Med Biol Eng Comput

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Adverse childhood experiences has been suggested to cause changes in physiological processes and can determine the magnitude of the stress response which might have a significant impact on health later in life. To detect the stress response, biomarkers that represent both Autonomic Nervous System (ANS) and Hypothalamic-Pituitary-Adrenal (HPA) axis is proposed. Among the available biomarkers, Heart Rate Variability (HRV) has been proven as a powerful biomarker that represents ANS. Meanwhile, salivary cortisol has been suggested as a biomarker that reflects the HPA axis. Even though many studies used multiple biomarkers to measure the stress response, the results for each biomarker were analysed separately. Therefore, the objective of this study is to propose a fusion of ANS and HPA axis biomarkers in order to classify the stress response based on adverse childhood experience. Electrocardiograph, blood pressure (BP), pulse rate (PR) and salivary cortisol (SCort) measures were collected from 23 healthy participants, 12 participants had adverse childhood experience while the remaining 11 acted as the no adversity control group. HRV was then computed from the ECG and the HRV features were extracted. Next, the selected HRV features were combined with the other biomarkers using Euclidean distance (e d) and serial fusion, and the performance of the fused features were compared using Support Vector Machine. From the result, HRV-SCort using Euclidean distance achieved the most satisfactory performance with 80.0% accuracy, 83.3% sensitivity and 78.3% specificity. Furthermore, the performance of the stress response classification of the fused biomarker, HRV-SCort, outperformed that of the single biomarkers: HRV (61% Accuracy), Cort (59.4% Accuracy), BP (78.3% accuracy) and PR (53.3% accuracy). From this study, it was proven that the fused biomarkers that represent both ANS and HPA (HRV-SCort) able to demonstrate a better classification performance in discriminating the stress response. Furthermore, a new approach for classification of stress response using Euclidean distance and SVM named as e d-SVM was proven to be an effective method for the HRV-SCort in classifying the stress response from PASAT. The robustness of this method is crucial in contributing to the effectiveness of the stress response measures and could further be used as an indicator for future health.

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Feature Selection for Heart Rate Variability Based Biometric Recognition Using Genetic Algorithm

Cited by 8 publications

References 18 publications

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

Stress Response Index for Traumatic Childhood Experience Based on the Fusion of Hypothalamus Pituitary Adrenocorticol and Autonomic Nervous System Biomarkers

Fusion of heart rate variability and salivary cortisol for stress response identification based on adverse childhood experience

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