Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques.

Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of the fetus is by Cardiotachography(CTG), cannot be used for long durations and continuous monitoring. There is a need for continuous and long duration monitoring of fetal ECG signals to study the progressive health status of the fetus using portable devices. The non-invasive method of electrocardiogram recording is one of the best method used to diagnose fetal cardiac problem rather than the invasive methods.The monitoring of the fECG requires development of a miniaturized hardware and a efficient signal processing algorithms to extract the fECG embedded in the mother ECG. The paper discusses a prototype hardware developed to monitor and record the raw mother ECG signal containing the fECG and a signal processing algorithm to extract the fetal Electro Cardiogram signal. We have proposed two methods of signal processing, first is based on the Least Mean Square (LMS) Adaptive Noise Cancellation technique and the other method is based on the Wavelet Transformation technique. A prototype hardware was designed and developed to acquire the raw ECG signal containing the mother and fetal ECG and the signal processing techniques were used to eliminate the noises and extract the fetal ECG and the fetal Heart Rate Variability was studied. Both the methods were evaluated with the signal acquired from a fetal ECG simulator, from the Physionet database and that acquired from the subject. Both the methods are evaluated by finding heart rate and its variability, amplitude spectrum and mean value of extracted fetal ECG. Also the accuracy, sensitivity and positive predictive value are also determined for fetal QRS detection technique. In this paper adaptive filtering technique uses Sign-sign LMS algorithm and wavelet techniques with Daubechies wavelet, employed along with de noising techniques for the extraction of fetal Electrocardiogram.Both the methods are having good sensitivity and accuracy. In adaptive method the sensitivity is 96.83, accuracy 89.87, wavelet sensitivity is 95.97 and accuracy is 88.5. Additionally, time domain parameters from the plot of heart rate variability of mother and fetus are analyzed.

Rapid identification of fluconazole resistance using Chrom agar Candida.

All though extremely rare 10 years ago, antifungal drug resistance is becoming a major problem in certain populations, especially in those infected with HIV. This study was undertaken to study the resistance of Candida species isolated in our hospital to Fluconazole using Chrom agar Candida. The Candida strains which were routinely isolated from clinical specimens like blood, urine, sputum, pus, fluid and homograft isolates were included in the study. 142 Candida isolates were tested by using Chrom agar Candida incorporated with Fluconazole. 16 strains were found to be resistant to Fluconazole and 126 strains sensitive to Fluconazole. Nine were C. tropicalis, 3 C. krusei, 2 C. guillermondii, 1 Geotrichum candidum and one was an unidentified strain of Candida. The MIC of the 16 strains were done using RPMI 1640 medium by macro broth dilution method. MIC of 9 strains was 64 & > 64 ug/ml of 6 strains 32 ug/ml and 1 strain 16 ug/ml.

Rapid identification of fluconazole resistance using Chromagar Candida.

Although extremely rare 10 years ago, antifungal drug resistance is becoming a major problem in certain populations, especially in those infected with HIV. This study was undertaken to study the resistance of Candida species isolated in our hospital to Fluconazole using Chrom agar Candida. The Candida strains which were routinely isolated from clinical specimens like blood, urine, sputum, pus, fluid and homograft isolates were included in the study. 142 Candida isolates were tested by using Chrom agar Candida incorporated with fluconazole. 16 strains were found to be resistant to Fluconazole and 126 strains sensitive to Fluconazole. Nine were C tropicalis, 3 C krusei, 2 C guillermondii, 1 Geotrichum candidum and one was an unidentified strain of Candida. The MIC of the 16 strains were done using RPMI 1640 medium by macro broth dilution method. MIC of 9 strains was 64 & > 64 micrograms/ml of 6 strains 32 micrograms/ml and 1 strain 16 micrograms/ml.