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Dive into the research topics where K. Ashoka Reddy is active.

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Featured researches published by K. Ashoka Reddy.


information sciences, signal processing and their applications | 2010

Evaluation of wavelets for reduction of motion artifacts in photoplethysmographic signals

M. Raghuram; K. Venu Madhav; E. Hari Krishna; K. Ashoka Reddy

Photoplethysmographic (PPG) signals obtained at Red and Infrared wavelengths are utilized in pulse oximetry for estimation of arterial blood oxygen saturation (SpO2). Mostly inaccurate readings in a pulse oximeter arise when PPG signals are contaminated with motion artifacts (MA) due to the movement of patient and hence MA are a common cause of oximeter failure and loss of accuracy. This paper presents performance evaluation of different wavelets for the reduction of MA. Test results on the PPG signals recorded with frequently encountered artifacts (viz., horizontal, vertical and bending motions of finger) reveal that the estimated Spo2 values from MA reduced PPGs using different wavelets are very close to each other. In addition, the Daubechies wavelet interestingly kept the respiratory information intact while effectively removing the MA from PPG signals. Hence, the work establishes that the Daubechies wavelet is the most preferred wavelet for pulse oximetry applications.


instrumentation and measurement technology conference | 2006

Virtual Instrument for the Measurement of Haemo-dynamic Parameters Using Photoplethysmograph

K. Ashoka Reddy; J. Rezuana Bai; Boby George; N. Madhu Mohan; V. Jagadeesh Kumar

This paper presents the design and development of a virtual instrument for the measurement of haemodynamic parameters namely, pulse rate and oxygen saturation in arterial blood based on the popular photoplethysmographic (PPG) principle. A clip-on sensor, housing red and infrared (IR) light emitting diodes and suitable photo detectors is developed. The sensor is interfaced to a PC utilizing the audio channel of the sound card, thus dispensing with expensive analog to digital converter hardware. Since the frequency response of the audio channel is not suitable for the PPG waveforms of red and IR, FM modulation and demodulation are employed. An empirical relationship is developed for the computation of the oxygen saturation in arterial blood using the red and IR PPG data and the well-known and well-established extinction coefficients of haemoglobin with and without oxygen. Data acquisition and processing are accomplished under LabVIEW virtual environment


instrumentation and measurement technology conference | 2011

Estimation of respiration rate from ECG, BP and PPG signals using empirical mode decomposition

K. Venu Madhav; M. Raghu Ram; E. Hari Krishna; Nagarjuna Reddy Komalla; K. Ashoka Reddy

Estimation of respiration rates from electrocardiogram (ECG), blood pressure (BP) and photoplethysmographic (PPG) signals would be an alternative approach for obtaining respiration related information. This process is useful in situations when, ECG, BP and PPG but not respiration is routinely monitored or in cases where, the cardiac arrhythmias are to be studied in correlation with respiratory information and is extremely important. There have been several efforts on ECG-Derived Respiration (EDR), BP-Derived Respiration (BDR) and PPG Derived Respiration (PDR). These methods are based on different signal processing techniques like filtering, wavelets and other statistical methods, which work by extraction of respiratory trend embedded into various physiological signals, as an additive component, or an amplitude modulated (AM) component or frequency modulated (FM) component. The proposed method is a robust, yet simple and makes use of derived Intrinsic Mode Functions (IMF) using Empirical Mode Decomposition (EMD). Test results on ECG, BP and PPG signals of the well known MIMIC database from Physiobank archive reveal that the proposed EMD method has efficiently extracted respiratory information from ECG, BP and PPG signals. The evaluated similarity parameters in both time and frequency domains for original and estimated respiratory rates have shown the superiority of the method.


IEEE Transactions on Instrumentation and Measurement | 2013

Robust Extraction of Respiratory Activity From PPG Signals Using Modified MSPCA

K. Venu Madhav; M. Raghu Ram; E. Hari Krishna; Nagarjuna Reddy Komalla; K. Ashoka Reddy

The pulse oximeters photoplethysmographic (PPG) signals can be well utilized for extracting the vital respiratory activity, in addition to saturation and heart rate estimations, avoiding the usage of additional sensor for recording respiratory signal, in turn reducing the number of sensors connected to the patients body for recording vital signals. In this paper, we present a robust algorithm called modified multi scale principal component analysis (MMSPCA), for extraction of respiratory activity embedded in the PPG signals. The PPG signals are more commonly corrupted by motion artifacts (MA) due to voluntary or involuntary movements of the patients, making it difficult for the algorithms to extract respiratory signals. The problem of extracting respiratory signals from PPGs in the presence of MA is addressed for the first time in this paper. The problem gets aggravated when PPGs are severely afflicted with MAs in situations such as the MA frequency band (usually below 0.2 Hz) overlapping on to the band of respiratory frequencies (0.2-0.4 Hz). In the presented algorithm, the kurtosis and energy contribution levels (ECL) of approximate and detail coefficients are calculated for each wavelet sub-band matrix, generating a modified wavelet sub-band matrix. This makes the presented algorithm based on MMSPCA more robust in the sense that it is made motion resistant by suitably modifying the MSPCA. Functioning of the proposed algorithm is tested on the data recorded from 15 healthy subjects. Each data set consists of intentionally created possible MA noises, viz., vertical, horizontal, waving, and pressing MAs with different breathing patterns. The method is also applied on the recordings available with MIMIC database of Physionet archive. The statistical and error analysis, performed to test the efficacy of the presented MMSPCA algorithm, revealed a very good acceptance for derived respiratory signal, when compared with the originally recorded respiratory signals using classical method. The MMSPCA method clearly outperformed the conventional MSPCA-based method in the presence of MA.


international conference on bioinformatics and biomedical engineering | 2010

On the Performance of Wavelets in Reducing Motion Artifacts from Photoplethysmographic Signals

M. Raghuram; K. Venu Madhav; E. Hari Krishna; K. Ashoka Reddy

In a pulse oximeter, clean artifact-free photoplethysmographic (PPG) signals with clearly separable DC and AC parts are necessary for error-free estimation of arterial oxygen saturation (SpO2). Motion artifacts (MA) introduced in the PPG signals due to the movement of a patient result in a significant error in the readings of pulse oximeters and hence are a common cause of oximeter failure and loss of accuracy. In this paper, we present a comparative analysis of using different wavelets for the case of MA reduction from corrupted PPG signals. PPG signals with frequently encountered artifacts (horizontal, vertical and bending motions of finger) were recorded from the subjects and processed with different wavelets. Simulation results and statistical analysis revealed that the Daubechies wavelet performed well for obtaining clean motion artifact-free PPG signal. Interestingly, the wavelet based method proved to be efficient in reducing MA restoring the respiratory information in tact with the PPG.


information sciences, signal processing and their applications | 2010

Acoustic echo cancellation using a computationally efficient transform domain LMS adaptive filter

E. Hari Krishna; M. Raghuram; K. Venu Madhav; K. Ashoka Reddy

Applications such as hands-free telephony, tele-classing and video-conferencing require the use of an acoustic echo canceller (AEC) to eliminate acoustic feedback from the loudspeaker to the microphone. Room acoustic echo cancellation typically requires adaptive filters with thousands of coefficients. Transform domain adaptive filter finds best solution for echo cancellation as it results in a significant reduction in the computational burden. Literature finds different orthogonal transform based adaptive filters for echo cancellation. In this paper, we present Hirschman Optimal Transform (HOT) based adaptive filter for elimination of echo from audio signals. Simulations and analysis show that HOT based LMS adaptive filter is computationally efficient and has fast convergence compared to LMS, NLMS and DFT-LMS. The computed Echo Return Loss Enhancement (ERLE), the general evaluation measure of echo cancellation, established the efficacy of proposed HOT based adaptive algorithm. In addition, the spectral flatness measure showed a significant improvement in cancelling the acoustic echo.


IEEE Transactions on Instrumentation and Measurement | 2013

ICA-Based Improved DTCWT Technique for MA Reduction in PPG Signals With Restored Respiratory Information

M. Raghu Ram; K. Venu Madhav; Ette Hari Krishna; Nagarjuna Reddy Komalla; K. Sivani; K. Ashoka Reddy

In addition to estimation of arterial blood oxygen saturation (SpO2), pulse oximeters photoplethysmographic (PPG) signals can be well utilized for extracting the vital respiratory information. The motion artifacts (MA) in PPGs not only make SpO2 estimations unreliable and inaccurate but also make it difficult to extract respiratory information. Addressing this issue, for the first time, we propose a novel approach called “ICA-based improved dual-tree complex wavelet transform (I2DTCWT)” technique, for efficient reduction of MAs leaving the respiratory information undisturbed. The method makes use of source separation ability of independent component analysis (ICA) along with computationally efficient modified DTCWT processing. A prototype pulse oximeter was developed and performance analysis of DTCWT, modified DTCWT and I2DTCWT processing methods was carried out using PPG data recorded with intentionally created MAs (horizontal MA, vertical MA, and bending MA). Experimental results demonstrated the efficiency of DTCWT processing methods in restoring PPG morphology and proved that there is a significant improvement guaranteed in reducing MAs with the presented methods. Statistical performance is evaluated in terms of measures like signal-to-noise ratio, normalized root mean square error, and correlation analysis with correlation co-efficient measure. The I2DTCWT outperformed other DTCWT processing methods in respect of MA reduction and the computed spectra revealed that safe extraction of respiratory information is guaranteed from these MA reduced PPGs. The proposed method is also validated by comparing with the well established signal extraction technology of MASIMO pulse oximeters, for which the discrete saturation transform (DST) is the key element. The %SpO2 estimations from processed PPGs by the proposed method closely followed the estimations based on DST and were very close to that of clean sections of PPG. In addition, the proposed method resulted in less computation cost compared to the MASIMO SET. Digital volume pulse waveform contour analysis is also performed on MA reduced PPGs to validate PPG morphology and the conventional parameters are calculated for assessing the arterial stiffness.


international conference on bioinformatics and biomedical engineering | 2010

Monitoring Respiratory Activity Using PPG Signals by Order Reduced-Modified Covariance AR Technique

K. Venu Madhav; M. Raghuram; E. Hari Krishna; K. Ashoka Reddy

The clinical significance of certain cardiac arrhythmias can be understood only with reference to respiration. In normal healthy conditions, the respiratory rate is 10-20 breaths/minute. But, certain problems of illness, accidents or some other causes affect the regular sinus rhythm. A non-invasive, non-occlusive and non-intrusive respiration monitoring is desirable in a number of situations such as ambulatory monitoring, stress tests and sleep disorder investigations. Such methods are based on deriving the respiratory activity from the electrocardiogram (ECG). There have been several efforts on ECG-Derived Respiration (EDR). Presently research is being focused on PPG derived respiratory activity because of its simplicity in sensing the signal and proved a strong correlation between PPG and respiratory signals. In this paper, we present an efficient method for extraction of respiratory activity from photoplethysmographic (PPG) signals based on modified covariance method. The proposed method makes use of order reduced AR-model by restricting the pole locations in the frequency range of interest. Test results reveal that the order reduced-modified covariance AR model (OR-MCAR) has efficiently separated respiratory information from PPG than a normal AR model.


ieee international symposium on medical measurements and applications | 2012

Dual-tree complex wavelet transform for motion artifact reduction of PPG signals

M. Raghuram; K. Venu Madhav; E. Hari Krishna; Nagarjuna Reddy Komalla; K. Sivani; K. Ashoka Reddy

Ever since the medical device pulse oximeter was invented, reliable and accurate estimation of arterial blood oxygen saturation (SpO2), based on the differential absorption of red/infrared light by hemoglobins, has been a challenging task. The Photoplethysmogram (PPG) waveform, also known as the “pulse oximetry waveform”, is well recognized for its use in pulse oximetry applications for the estimation of SpO2 and can be obtained noninvasively and continuously in a comfortable manner using low cost & portable PPG sensors. Inaccuracy in the estimation of SpO2 may prevail due to the motion artifacts (MA) corruption in the detected PPG signals by the intentional or unintentional movements of a patient. The MA noise corruption is unavoidable while recording the PPGs because of a very small pulsatile component in PPG (0.1% of total signal amplitude) and it can be reduced by suitable processing of the PPG signals. In this paper, an approach for motion artifact (MA) reduction of photoplethysmographic (PPG) signals based on the concept of dual-tree complex wavelet transform technique is proposed. Experimental results revealed that DTCWT processing of MA corrupted PPGs outperformed the db10 wavelet processing for MA reduction of PPG signals and can be referred as best suitable MA reduction technique for pulse oximetry applications.


international conference on communications | 2011

Extraction of respiratory activity from PPG and BP signals using Principal Component Analysis

K. Venu Madhav; M. Raghu Ram; E. Hari Krishna; K. Nagarjuna Reddy; K. Ashoka Reddy

In high risk situations such as cardiac arrhythmias, ambulatory monitoring, stress tests, sleep disorder investigations and post-operative hypoxemia situations, monitoring of respiratory activity would be mandatory. Electrocardiogram (ECG), blood pressure (BP) and photoplethysmographic (PPG) signals can be used for extraction of respiratory activity, and will eventually eliminate the use of additional respiratory sensor. Using a simple and standard non-parametric mathematical technique, Principal Component Analysis (PCA), the respiratory related information is extracted from complex data sets such as PPG and BP signals. The respiratory induced variations (RIV) of PPG and BP signals are described by coefficients of computed principal components. Singular value ratio (SVR) trend is used to find the periodicity, which is one of the crucial parameters in forming the data sets for PCA. Test results on MIMIC data base clearly indicated a strong correlation between the extracted and actual respiratory signals. Statistical measures in both time and frequency domains such as Relative Correlation Coefficient (RCC) and Magnitude Squared Coherence (MSC) respectively and Accuracy Rate (AR) are calculated to demonstrate the fact, that respiratory signal is present in the form of first principal components.

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K. Venu Madhav

Kakatiya Institute of Technology and Science

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K. Sivani

Kakatiya Institute of Technology and Science

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M. Raghu Ram

Kakatiya Institute of Technology and Science

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M. Raghuram

Kakatiya Institute of Technology and Science

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G. Rajender Naik

Kakatiya Institute of Technology and Science

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Boby George

Indian Institute of Technology Madras

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Ch. Navitha

Kakatiya Institute of Technology and Science

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N. Madhu Mohan

Indian Institute of Technology Madras

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