Jayashree Santhosh

Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography

Heart rate variability (HRV) is traditionally derived from RR interval time series of electrocardiography (ECG). Photoplethysmography (PPG) also reflects the cardiac rhythm since the mechanical activity of the heart is coupled to its electrical activity. Thus, theoretically, PPG can be used for determining the interval between successive heartbeats and heart rate variability. However, the PPG wave lags behind the ECG signal by the time required for transmission of pulse wave. In this study, finger-tip PPG and standard lead II ECG were recorded for five minutes from 10 healthy subjects at rest. The results showed a high correlation (median = 0.97) between the ECG-derived RR intervals and PPG-derived peak-to-peak (PP) intervals. PP variability was accurate (0.1 ms) as compared to RR variability. The time domain, frequency domain and Poincaré plot HRV parameters computed using RR interval method and PP interval method showed no significant differences (p < 0.05). The error analysis also showed insignificant differences between the HRV indices obtained by the two methods. Bland-Altman analysis showed high degree of agreement between the two methods for all the parameters of HRV. Thus, HRV can also be reliably estimated from the PPG based PP interval method.

Development of an expert multitask gadget controlled by voluntary eye movements

Design of assistive technology using advanced soft computing techniques on proper hardware platform has been an important issue of research for the last two decades. In the present study, a novel scheme is presented to develop a multitask gadget controlled by eye movements for the disabled, especially for individuals with spinal injury disorders. Electro-oculogram (EOG) signals generated by horizontal, vertical and diagonal eye movements and blinks were measured using a pair of surface electrodes with respect to a reference electrode placed on forehead. After preprocessing, the acquired signals were amplified with AC-coupling in order to reduce unnecessary drifts. Classifier based on DFA (Deterministic Finite Automata) was developed by using VHDL to discriminate 128 different EOG states from processed horizontal and vertical eye signals based on threshold settings specific to individuals. Later, online viability of the system was established by conducting some experiments on normal as well as disabled subjects. The utility of the proposed method was enhanced by implementing a robust algorithm for signal classification and training both the subjects and the device. It was found that with the proposed scheme, the accuracy of the detection and control of the specified gadget is 95.33%, with sensitivity and specificity as 95.6% and 95%, respectively. The proposed model can be used for designing smart houses for the disabled and elderly.

Nadi Yantra: A Robust System Design to Capture the Signals from the Radial Artery for Non-Invasive Diagnosis

Radial artery is one of the most significant arteries for non-invasive diagnosis for ages. Ayurvedic and other alternative medical practitioners throughout the world have been diagnosing diseases and the organ at distress by feeling the palpations at three close yet precise positions of the radial artery. This paper presents a robust system Nadi Yantra1 that monitors pressure variations from the points of interest for pulse diagnosis, thereby eliminating subjective errors and overcomes the limitation of mastering the art of pulse diagnosis with experience. This system comprises of three identical piezo based sensors, amplifier and filter circuit, a mechanical set-up and a data acquisition system (BioPac-150trade) which captures the signal from those three precise positions of the radial artery thereby giving an objective approach to the science of pulse diagnosis. Morphology of the waveforms obtained from our system concurs with standard physiological arterial signals. Our technique reveals subtle variations in real time that holds promise in quantitative assessment of pulse. Advances over the earlier systems are that Nadi Yantra allows recording for hours by an automated external pressure on the three positions thereby completely removing the potential for errors incurred when a person does the job of applying pressure. The locking mechanism significantly resists the motion artifacts as well. Repeatability, reproducibility and stability of the system has been verified. Signal processing techniques were applied to obtain morphological features such as amplitude, power spectral density, bandpower and spectral centroid to reflect variations in signals from the three channels. Data acquired from the system at three points gives unique information and can be used for disease diagnosis.

Influence of respiratory rate on the variability of blood volume pulse characteristics

Photoplethysmogram (PPG) measures have been proven useful for the quantification of sympathetic reactivity and continuous monitoring of vascular reactivity. This study was designed to delineate the influence of respiratory rate on the variability of various PPG characteristics in time and frequency domains. PPG, electrocardiogram (ECG) and respiration were simultaneously recorded for 2 min from eight healthy volunteers during paced respiration of 6, 12 and 18 cycles min−1. The PPG characteristics such as peak-to-peak interval (PPI), systolic peak amplitude, slope, Tcrest, Tdecay and pulse transit time (PTT) were computed for every pulse. In time domain, the mean of amplitude, slope and Tcrest were not significantly different amongst three different respiratory rates. However, the mean of Tdecay, PPI and PTT were significantly increased (p < 0.05, p < 0.05 and p < 0.01, respectively) during respiration of 6 cycles min−1 compared to 12 cycles min−1. The maximal spectral powers of the variability of all PPG measures were centred on the respiratory frequency in frequency domain. In conclusion, the results that the amplitude and slope in time domain are not altered by the respiratory frequency suggest their application in faithful assessment of cardiovascular reactivity. As the variability of PPI, Tdecay and PTT are altered by the slow respiration, the influence of respiration on these time derivatives should not be ignored during interpretation of vascular reactivity.

Monitoring of Cardiovascular Reactivity During Cold Pressor Test Using Photoplethysmography

Cold pressor test (CPT) is a standard autonomic function test to assess the sympathetic activity. The rise in diastolic blood pressure, secondary to the vasoconstriction in the peripheral vessels has been traditionally used to quantify the sympathetic reactivity to cold stimulus. In the present study, the ongoing short-term changes in vascular condition during CPT have been directly studied using waveform analysis of infrared finger-tip photoplethysmogram (PPG). Electrocardiogram (ECG) and PPG were simultaneously recorded from 11 healthy volunteers in resting condition (1 min), during (1 min) and after (5 min) the test. The PPG characteristics such as peak-to-peak interval (PPI), peak amplitude, slope, crest time and decay time were computed for every PPG pulse. In addition, the beat-to-beat changes of pulse wave transit time (PTT) representing the vascular compliance were also computed. The results depicted that the mean PPI, decay time and PTT were significantly different (respectively p≪0.01, p≪0.05 and p≪0.01) during CPT compared to rest. A sharp significant change was noticed in the amplitude (3.77±1.50 to 0.90±0.41 V) and slope (48.39±23.86 to 12.33±6.05 V/s) in response to the cold stimulus followed by a slow recovery phase. These results suggest that PPG based objectively derived parameters can be used for quantification of sympathetic reactivity and for continuous monitoring of vascular reactivity.

Wavelet based compression technique of Electro-oculogram signals

In this paper, we present a compression technique for the acquired Electro-oculogram (EOG) signals. Researchers in the past have mainly focused on the EOG signals while dealing with Electroencephalogram signals for the removal of Ocular Artifacts. From a new perspective, a scheme pivoted on multi-resolution analysis and the Wavelet Transform theory was used essentially to process and enhance nonstationary and time-varying EOG Signals. Coiflet wavelets were used for the compression of EOG signals by thresholding of wavelet coefficients.

Feasibility of Photoplethymographic Signal for Assessment of Autonomic Response using Heart Rate Variability Analysis

Heart rate variability (HRV) represents one of the most promising quantitative markers of autonomic nervous system activity. It is commonly derived from RR interval of electrocardiographic (ECG) signal to evaluate the balance of sympathetic and parasympathetic responses due to the change in heart rhythm. However, in this present study, peak-to-peak interval variability of photoplethysmographic (PPG) signal has been used for the estimation of HRV. Further, to demonstrate the accuracy and feasibility of HRV extraction from PPG signal, finger-tip PPG and standard lead II ECG signals were simultaneously acquired under normal and deep breathing conditions. A comparative analysis of time- and frequency-domain measures of HRV was carried out. The correlation analysis of tachograms of ECG and PPG (Pearson linear correlation coefficient 0.9698 and 0.7389 under normal and deep respiration respectively) and error analysis of HRV parameters suggest that PPG can be considered as a simpler and reliable alternative for the HRV analysis and estimation of autonomic regulation.

Portable cost-effective EEG data acquisition system

Neuro-cognitive dysfunctions are common clinical abnormalities found in society. They require objective analysis by various instruments; an important technique involves monitoring electroencephalogram (EEG) signals. To date, EEG machines have been robust, costly and require patients to come to a hospital for test. Therefore, we have constructed a simple, cheap and portable EEG instrument for wider patient use. It consists of two active digital EEG probes with two channels each, making it a four-channel portable acquisition system. It is further connected through a two-wire serial bus to the acquisition unit, which comprises an analogue to digital converter (ADC) and an ARM board processor with 2 GB memory and USB interface. The whole system is placed in a small box making it highly portable for wider use in clinical settings.

Enhancement of inter-hemispheric brain waves synchronisation after Pranayama practice

Pranayama practices are being used since ancient times as a holistic approach by saints and yogis to improve and control the subtle phenomenon of the brain and hence to bring mind and body in synchrony using breath as a link. This work is an attempt to understand this subtle phenomenon of the brain and the process of attaining higher state of cognition followed by consciousness. From the Electroencephalogram (EEG) signal analysis, it was observed that with the increase in Pranayama practices, the frequency of oscillation shifts from lower-frequency range to higher-frequency range with the significant rise of gamma power (>40 Hz) in frontal, central and also some part of temporal region of the brain. This frequency shifts count for attaining higher cognitive states leading to consciousness of human brain.

Dynamical neural activation in human brain during face recognition

This paper presents an experimental model and a set of stipulations for understanding neural progression in human brain while distinguishing familiar faces, and relationship between recognition and other aspects of face processing. Dynamical imagery stimuli of familiar and unfamiliar faces were shown to healthy individuals and were asked to recognise them as quickly and accurately as possible. Results obtained from the non-parametric analysis of the recorded multivariate data indicate that process of structural decoding of unfamiliar faces occurring inside the brain is delayed in comparison with familiar face probably due to few distinctive information that we derive from seen faces appear to influence the processing performance of the brain during the task.