Dr Rashid Afkhami

Reflected pressure waves are key to the understanding of vascular ageing, a prominent factor in major cardiovascular events. Several different metrics have been proposed to index the effect of wave reflection on the pressure waveform and thereby serve as an indicator of vascular ageing. The extent to which these indices are influenced by factors other than vascular health remains a matter of concern. In this paper, we use transmission-line theory to derive a mathematical model for the reflection time (Trefl), and the augmentation index (AI), assuming a general extended model of the arterial system. Then, we test the proposed model against values reported in the literature. Finally, we discuss insights from the model to common observations in the literature such as age-related ¿shift¿ in the reflection site, the variation of AI with heart rate, and the flattening of Trefl in older participants.

Near-infrared spectroscopy (NiRS) is a relatively new technology of brain imaging with its potential in the assessment of cerebrovascular health only recently discovered. Encouraging early results suggest that NiRS can be used as an inexpensive and portable cerebrovascular health tracking device using a recently proposed pulse relaxation function (PReFx). In this paper, we propose a new NiRS timing index, TI NiRS, of cerebrovascular health. TI NiRS is a novel use of the NiRS technology. TI NiRS is motivated by the previously proved relationship of the timing of the reflected wave with vascular resistance and compliance in the context of pressure waveforms. We correlated both TI NiRS and PReFx against age, a non-exercise cardiorespiratory fitness (CRF) index, and two existing indices of cerebrovascular health, namely transcranial Doppler (TCD) augmentation index, AI TCD, and magnetic resonance imaging (MRI) blood flow pulsatility index, PI MRI. The TI NiRS correlations with Age, CRF, PI MRI and AI TCD all are significant, i.e., r= 0.53 (p= 0.002), r= - 0.44 (p= 0.011), r= 0.45 (p= 0.012) and r= 0.46 (p= 0.010), respectively. PReFx, however, did not have significant correlations with any of the vascular health factors. The proposed timing index is a reliable indicator of cerebrovascular aging factors in the NiRS waveform.

Reflected pressure waves are key to the understanding of vascular ageing, a prominent factor in major cardiovascular events. Several different metrics have been proposed to index the effect of wave reflection on the pressure waveform and thereby serve as an indicator of vascular ageing. The extent to which these indices are influenced by factors other than vascular health remains a matter of concern. In this paper, we use transmission-line theory to derive a mathematical model for the reflection time (Trefl), and the augmentation index (AI), assuming a general extended model of the arterial system. Then, we test the proposed model against values reported in the literature. Finally, we discuss insights from the model to common observations in the literature such as age-related ¿shift¿ in the reflection site, the variation of AI with heart rate, and the flattening of Trefl in older participants.

Transcranial Doppler (TCD) blood flow velocity has been extensively used in biomedical research as it provides a cost-effective and relatively simple approach to assess changes in cerebral blood flow dynamics and track cerebrovascular health status. In this article we introduce a new TCD-based timing index, TITCD, as an indicator of vascular stiffening and vascular health. We investigate the correlations of the new index and the existing indices, namely the pulsatility index and the augmentation index, with age, cardiorespiratory fitness (CRF) and magnetic resonance imaging (MRI) blood flow pulsatility index (PIMRI). Notably, the new index showed stronger correlations with CRF (r = -0.79) and PIMRI (r = 0.53) compared with the augmentation index (r = -0.65 with CRF and no significant correlation with PIMRI) and the pulsatility index (no significant correlations with CRF or PIMRI), and a similar correlation with age as the augmentation index. The clearer relationship of the proposed timing index with vascular aging factors underlines its utility as an early indicator of vascular stiffening.

Electrocardiogram (ECG) is a popular non-invasive test record, which shows the electrical activities of the heart. In this paper we propose a novel method to detect left ventricular hypertrophy (LVH) with the use of ECG. Left ventricular hypertrophy is defined as the enlargement of the left ventricle, which is a common disease among hypertension patients. Proposed algorithm uses discrete wavelet transform (DWT) to extract morphological features of ECG signal and exploits higher order statistic (HOS) features including kurtosis, skewness and 5th moment. These features are fed to a support vector machine (SVM) classifier with kernel function of radial basis function (RBF). Our method has been tested on a large database of ECG signals and we have obtained the highest accuracy of 99.6% and sensitivity of 99.4%.