A forward body bias generator for digital CMOS circuits with supply voltage scaling

We propose a new fully-integrated forward body bias (FBB) generator that holds its voltage constant relative to the (scalable) power supply of a digital IP. The generator is modular and can drive distinct digital IP block sizes in multiples of up to 1mm2. The design has been implemented in 90nm low-power CMOS. Our basic unit for driving digital IP blocks up to 1mm2 occupies a silicon area of 0.03mm2 only. The generator completes a 500mV FBB voltage step within 4µs. The bandwidth of the design is 570kHz. The active current of the FBB generator alone is about 177µA for a nominal process, 1.2V supply and 85°C. The standby current is as low as 72nA at 27°C

Heartbeat classification using disease-specific feature selection

<span lang="EN-US" style="font-family: &quot;Calibri&quot;,&quot;sans-serif&quot;; font-size: 10.5pt; mso-bidi-font-size: 11.0pt; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: 宋体; mso-fareast-theme-font: minor-fareast; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: &quot;Times New Roman&quot;; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;"><font color="#000000">Automatic heartbeat classification is an important technique to assist doctors to identify ectopic heartbeats in long-term Holter recording. In this paper, we introduce a novel disease-specific feature selection method which consists of a one-versus-one (OvO) features ranking stage and a feature search stage wrapped in the same OvO-rule support vector machine (SVM) binary classifier. The proposed method differs from traditional approaches in that it focuses on the selection of effective feature subsets for distinguishing a class from others by making OvO comparison. The electrocardiograms (ECG) from the MIT-BIH arrhythmia database (MIT-BIH-AR) are used to evaluate the proposed feature selection method. The ECG features adopted include inter-beat and intra-beat intervals, amplitude morphology, area morphology and morphological distance. Following the recommendation of the Advancement of Medical Instrumentation (AAMI), all the heartbeat samples of MIT-BIH-AR are grouped into four classes, namely, normal or bundle branch block (N), supraventricular ectopic (S), ventricular ectopic (V) and fusion of ventricular and normal (F). The division of training and testing data complies with the inter-patient schema. Experimental results show that the average classification accuracy of the proposed feature selection method is 86.66%, outperforming those methods without feature selection. The sensitivities for the classes N, S, V and F are 88.94%, 79.06%, 85.48% and 93.81% respectively, and the corresponding positive predictive values are 98.98%, 35.98%, 92.75% and 13.74% respectively. In terms of geometric means of sensitivity and positive predictivity, the proposed method also demonstrates better performance than other state-of-the-art feature selection methods. (C) 2013 Elsevier Ltd. All rights reserved.</font></span