Bandana Majumdar

Security analysis and implementation of web-based telemedicine services with a four-tier architecture

Security of telemedicine applications is not often given adequate importance by the developers and healthcare administrators primarily to reduce cost. Though some security safeguards are employed by these applications to comply with existing medical data security and privacy regulations, these are not adequate in todaypsilas context. Moreover, in a Web-based application environment not only the data but also the application itself is vulnerable to attackers. Keeping these concerns in mind, we present the design of a Web-based, four-tier telemedicine system named iMedik which is accessible over desktops as well as handheld devices. We have illustrated how the proposed system differs from existing three-tier Web applications. The compliance status of the application with HIPAA Security Guidelines has also been noted. The security measures described in our approach look into the four-tier architecture from an attackerpsilas viewpoint and present a simple road map for developing secure e-health application with anywhere, anytime availability.

Feature Selection for Automatic Burst Detection in Neonatal Electroencephalogram

Monitoring neonatal electroencephalogram (EEG) signal is useful in identifying neonatal convulsions which might be clinically invisible. Presence of burst suppression pattern in neonate EEG is a clear indication of epilepsy. Visual identification of burst patterns from recorded continuous raw EEG data is time consuming. On the other hand, automatic burst detection techniques mentioned in the standard literature mostly rely on comparison with respect to predefined static voltage or energy thresholds, thus becoming too specific. Burst detection using ratio information of quantitative feature values between burst segment and neighborhood background EEG segment is proposed in this paper. Features like ratio of mean nonlinear energy, power spectral density, variance and absolute voltage, when applied as an input to a support vector machine (SVM) classifier, provides high degree of separability between burst and normal (nonburst) EEG segments. Exhaustive simulation using various literature specified features and proposed feature combinations shows that the proposed feature set provides best classification accuracy compared to other reported burst detection methods. The results documented in this paper can be used as a reference of optimum quantitative EEG feature sets for distinguishing between burst and normal (nonburst) EEG segments.

Achieving e-health care in a distributed EHR system

In modern health care, use of web based EHR (Electronic Health Record) system has increased remarkably because of its world-wide accessibility and the facility of the collaborative work among multiple users. Major drawbacks of such centralized web based system are link failure and low or no fault tolerance. In an unreliable network, it is very commonplace that service is unavailable due to connection failure. The problem motivates us to devise a decentralized system that would work seamlessly in unreliable Internet infrastructure. In this paper, we have presented such an e-healthcare system (named iMedikD) which supports both local and centralized access where hospitalwise responsibility is delegated among multiple peripheral servers. Therefore, by lowering the dependency over the external links the system is able to keep the peripheral services running uninterrupted. Only in case of remote referral, peripheral servers communicate through external links to web based servers with a view to minimize bandwidth utilization.

Detection of artifacts from high energy bursts in neonatal EEG

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well.

A user friendly implementation for efficiently conducting Hammersmith Infant Neurological Examination

The aim of this work is to design a semi-automatic application that can be used as an aid by the doctors for smoothly conducting Hammersmith Infant Neurological Examination (HINE). A simplified version of the examination which provides a quantitative neurological assessment is used to design the application. The application includes a methodology of conducting HINE examination suited to inexperienced staff, applicable to both neonatal and post-neonatal infants. It also provides a facility to go through the previous records of a patient that can help in diagnosing patients with high risk of neurological disorder. A semi-automatic approach is proposed for skeleton generation. The application has been installed in hospitals and currently in operation. It is expected to increase the efficiency of conducting HINE using the proposed application.

PDA Based Telemedicine System in a Web Based Environment

updation of the same at the point of patient care is of immense importance. Personal Digital Assistant (PDA) can be a promising information delivery tool to achieve this. We have developed a wireless telemedicine system, which supports medical data access using PDA. The system uses the database of Web Based TelemediK System (developed at IIT Kharagpur). This paper describes the system architecture of the wireless telemedicine system, its features and the result of an experiment where the usability of the proposed PDA based system is tested and compared with the Web Based TelemediK System in a desktop environment. Keywords- PDA, Web Based, Telemedicine, Usability Study.

A Web Enabled Health Information System for the Neonatal Intensive Care Unit (NICU)

Information Systems are needed for modernization of ICUs to deliver better health care services. EHR systems can improve the work flow management in health care delivery. This work proposes a secure web enabled system based on a multi-tier architecture for carrying out routine and special operations of Neonatal Intensive Care Unit (NICU). The system adopts a service oriented approach for execution of various tasks that are performed for managing NICU activities. It also facilitates decision support systems for a number of critical tasks of NICU. A prototype of the system has been installed in the neonatology department of SSKM Hospital, Kolkata, India and the staff of the hospital including doctors, nurses, laboratory personals and technicians are using it in a regular manner.

Summarization of Neonatal Video EEG for Seizure and Artifact Detection

Monitoring neonatal EEG signal is useful in identifying neonatal convulsions or seizures. For neonates, seizures can be electrographic, electro clinical, or both simultaneously. Electrographic seizure is identified via recorded EEG signal, while electro clinical seizures exhibit clinical manifestations. Sometimes neonates can exhibit silent seizures which may be clinically invisible but identifiable in recorded EEG, or vice versa. Thus, simultaneous monitoring of video and recorded EEG determines the correlation between the electrographic and electro clinical seizures. Furthermore, analyzing the movements of the neonates can identify movement artifacts easily, thus preventing false seizure detection. However, storage of high quality video recordings require large storage space. As neonates do not commonly exhibit movements, summarizing the video for storing only patient movements along with corresponding timestamps, can be useful. In this paper, a video summarization method is proposed for efficient browsing of video-EEG. Identification and analysis of the patterns of interest is possible via summarized information, thus reducing effective analysis time. In addition, quantitative demonstration of electrographic and electro clinical seizures is presented to analyze the utility of video-EEG.

A Tool for Automatic Hammersmith Infant Neurological Examination

Hammersmith Infant Neurological Examination HINE is a popular method to estimate the neurological development of infants aged less than two years. Using HINE, especially for preterm or premature babies, the risk of neural disorder can be minimized through proper preventive measures. This paper presents the design of a semi-automatic application that can be used as an aid to doctors for efficiently conducting the examinations listed in the Hammersmith chart. The user friendly version of the examination interface provides a platform for quantitative neurological assessment of the infants. It includes various simplified video and image based schemes that are suited to inexperienced staff. It provides an interface to go through the previous records of patients. Ten examinations are enlisted in the Hammersmith chart for neonatal babies. This paper examines a semi-automatic approach for posture estimation examination. For post neonatal infants, a follow-up management interface is designed that can be used to fetch / consult past records of the patients for better diagnosis. The application is currently in operation at Neonatal Intensive Care Unit NICU of Institute of Post-Graduate Medical Education & Research IPGME & R and Seth Sukhlal Karnani Memorial SSKM Hospital, Kolkata, India.

Real Time Medical Image Consultation System Through Internet

Teleconsultation among doctors using a telemedicine system typically involves dealing with and sharing medical images of the patients. This paper describes a software tool written in Java which enables the participating doctors to view medical images such as blood slides, X-Ray, USG, ECG etc. online and even allows them to mark and/or zoom specific areas. It is a multi-party secure image communication system tool that can be used by doctors and medical consultants over the Internet.