Rohan Banerjee

Noise cleaning and Gaussian modeling of smart phone photoplethysmogram to improve blood pressure estimation

Photoplethysmography (PPG) signals, captured using smart phones are generally noisy in nature. Although they have been successfully used to determine heart rate from frequency domain analysis, further indirect markers like blood pressure (BP) require time domain analysis for which the signal needs to be substantially cleaned. In this paper we propose a methodology to clean such noisy PPG signals. Apart from filtering, the proposed approach reduces the baseline drift of PPG signal to near zero. Furthermore it models each cycle of PPG signal as a sum of 2 Gaussian functions which is a novel contribution of the method. We show that, the noise cleaning effect produces better accuracy and consistency in estimating BP, compared to the state of the art method that uses the 2-element Windkessel model on features derived from raw PPG signal, captured from an Android phone.

Estimating blood pressure using Windkessel model on Photoplethysmogram.

Simple and non-invasive methods to estimate vital signs are very important for preventive healthcare. In this paper, we present a methodology to estimate Blood Pressure (BP) using Photoplethysmography (PPG). Instead of directly relating systolic and diastolic BP values with PPG features, our proposed methodology initially maps PPG features with some person specific intermediate latent parameters and later derives BP values from them. The 2-Element Windkessel model has been considered in the current context to estimate total peripheral resistance and arterial compliance of a person using PPG features, followed by linear regression for simulating arterial blood pressure. Experimental results, performed on a standard hospital dataset yielded absolute errors of 0.78±13.1 mmHg and 0.59 ± 10.23 mmHg for systolic and diastolic BP values respectively. Results also indicate that the methodology is more robust than the standard methodologies that directly estimate BP values from PPG signal.

Smartphone Based Digital Stethoscope for Connected Health -- A Direct Acoustic Coupling Technique

Mobile smartphones have revolutionized the concept of mobile phones as different apps are built to offer various interesting applications in healthcare, gaming, etc. rather than using the phone only for voice services. The application developers take advantage of onboard sensors, web connectivity and powerful processing units of the smartphones to develop such interesting apps. In this paper, we present an interesting approach where direct acoustic coupling technique is employed to quickly and conveniently convert the smartphones into high quality digital stethoscope by using an ultra-low cost attachment. The design consideration primarily focused on affordability, simplicity and use-friendly aspects into account. The motivation of this work is to enable the heart patients to send their heart sound to doctors chamber/ hospitals from their home instead of travelling all the way to hospitals/clinics. This is particularly useful in poor or developing countries where there is scarcity of healthcare centers and patients have to travel a long distance to visit the clinic. The situation becomes more serious in case of patients who went through heart surgery and requires follow up visits or for elderly population requiring routine checkups. For both the cases, in many instances, doctors primarily listen to the heart sound to prescribe further actions. Our solution can greatly help such situations and in many cases it can completely eliminate the travel. In addition, since data can also be recorded and saved, this opens up possibility for statistical analysis to further aid the diagnosis / monitoring. Thus, our solution can serve as a viable tool in connected health use cases for heart patients for needy geography.

Feasibility Analysis for Estimation of Blood Pressure and Heart Rate using A Smart Eye Wear

The major issue with hypertension is the fact that it does not have any symptoms also measurements are highly variable in nature. However, high blood pressure has severe effects on a persons health. This poses a requirement for continuous blood pressure monitoring and analysis of people suffering from chronic condition. In this paper we propose PPG based continuous blood pressure and heart rate monitoring system in form factor of a Smart Eye wear. We do a feasibility analysis of the idea and show that it is possible to determine blood pressure and heart rate by sensing PPG from the side of head region (temple) where a spectacle frame would fit. The advantage of Smart Eye Wear over other wearable device is that it provides better contact, minimal motion artifact and maintains uniform pressure without causing any trouble to user.

Identifying coronary artery disease from photoplethysmogram

This paper presents the idea of a non invasive screening system for identifying Coronary Artery Disease (CAD) patients from fingertip Photoplethysmogram (PPG) signal. A combined feature set, related to heart rate variability (HRV) as well as shapes of PPG waveform has been defined for distinguishing CAD and non CAD subjects. Support Vector Machine (SVM) is used for classification. Our methodology yields sensitivity and specificity scores of 0.82 and 0.88 respectively in identifying CAD patients on a corpus of 112 subjects, selected from MIMIC II dataset. Further, we achieved sensitivity and specificity scores of of 0.73 and 0.87 on another dataset of 30 subjects, collected from an urban hospital using commercial oximeter device.

PhotoECG: Photoplethysmographyto estimate ECG parameters

This paper presents a simple method to indirectly estimate the range of certain important electrocardiogram (ECG) parameters using photoplethysmography (PPG). The proposed method, termed as PhotoECG, extracts a set of time and frequency domain features from fingertip PPG signal. A feature selection algorithm utilizing the concept of Maximal Information Coefficient (MIC) is presented to rank the PPG features according to their relevance to create training models for different ECG parameters. The proposed method yields above 90% accuracy in estimating ECG parameters on a benchmark hospital dataset having clean PPG signal. The same method results an average of 80% accuracy on noisy PPG signal captured by iPhone, indicating its feasibility to create phone applications for preventive ECG monitoring at home.

Blood pressure estimation from photoplethysmogram using latent parameters

Non-invasive cuff-less Blood Pressure (BP) estimation from Photoplethysmogram (PPG) is a well known challenge in the field of affordable healthcare. This paper presents a set of improvements over an existing method that estimates BP using 2-element Windkessel model from PPG signal. A noisy PPG corpus is collected using fingertip pulse oximeter, from two different locations in India. Exhaustive pre-processing techniques, such as filtering, baseline and topline correction are performed on the noisy PPG signals, followed by the selection of consistent cycles. Subsequently, the most relevant PPG features and demographic features are selected through Maximal Information Coefficient (MIC) score for learning the latent parameters controlling BP. Experimental results reveal that overall error in estimating BP lies within 10% of a commercially available digital BP monitoring device. Also, use of alternative latent parameters that incorporate the variation in cardiac output, shows a better trend following for abnormally low and high BP.

Smart phone based blood pressure indicator

In this paper, we propose a methodology to estimate the range of human blood pressure (BP) using Photoplethysmography (PPG). 12 time domain features and 7 frequency domain features are pointed out and extracted from the PPG signal. A feature selection algorithm based on Maximal Information Coefficient (MIC) is presented to reduce the dimensionality of the feature set to effective ones, thereby cutting down resource requirements. Support Vector Machine (SVM) is used to classify the BP values into separate bins. The proposed methodology is validated and tested on a standard benchmark clean dataset as well as phone captured noisy dataset to justify its robustness and efficiency. Apart from a commending performance improvement, BP estimation is achieved with minimal features and processing, making the algorithm light weight for porting on smart phones.

HeartSense: smart phones to estimate blood pressure from photoplethysmography

In this paper we propose to demonstrate a smart phone application, that estimates human blood pressure (BP) values from photoplethysmography (PPG) signal using Windkessel model. PPG signal is extracted from a video sequence of a users index fingertip, acquired using smart phone camera. A set of time domain PPG features are used to estimate different lumped parameters of Windkessel model to simulate arterial BP. Under most of the cases, the application estimates systolic and diastolic BP values, within a range of ±10% of clinical measurement.

Estimation of ECG parameters using photoplethysmography

Regular ECG check up is a good practice for cardiac patients as well as elderly people. In this paper we propose a low cost methodology to coarsely estimate the range of some important parameters of ECG using Photoplethysmography (PPG). PPG is easy to measure (even with a smart phone) and strongly related to human cardio-vascular system. The proposed methodology extracts a set of time domain features from PPG signal. A statistical analysis is performed to select the most relevant set of PPG features for the ECG parameters. Training model for the ECG parameters are created based on those selected features. Both artificial neural network and support vector machine based supervised learning approach is used for performance comparison. Experimental results, performed on benchmark dataset shows that good accuracy in the estimation of ECG parameters can be achieved in our proposed methodology. Results also show that the overall performance improves in using feature selection technique rather than using all the PPG features for classification.