Pseudo Asynchronous Level Crossing adc for ecg Signal Acquisition

A new pseudo asynchronous level crossing analogue-to-digital converter (adc) architecture targeted for low-power, implantable, long-term biomedical sensing applications is presented. In contrast to most of the existing asynchronous level crossing adc designs, the proposed design has no digital-to-analogue converter (dac) and no continuous time comparators. Instead, the proposed architecture uses an analogue memory cell and dynamic comparators. The architecture retains the signal activity dependent sampling operation by generating events only when the input signal is changing. The architecture offers the advantages of smaller chip area, energy saving and fewer analogue system components. Beside lower energy consumption the use of dynamic comparators results in a more robust performance in noise conditions. Moreover, dynamic comparators make interfacing the asynchronous level crossing system to synchronous processing blocks simpler. The proposed adc was implemented in [Formula: see text] complementary metal-oxide-semiconductor (cmos) technology, the hardware occupies a chip area of 0.0372 mm² and operates from a supply voltage of [Formula: see text] to [Formula: see text]. The adc's power consumption is as low as 0.6 μW with signal bandwidth from [Formula: see text] to [Formula: see text] and achieves an equivalent number of bits (enob) of up to 8 bits.

Bufferless Compression of Asynchronously Sampled ECG Signals in Cubic Hermitian Vector Space

Asynchronous level crossing sampling analog-to-digital converters (ADCs) are known to be more energy efficient and produce fewer samples than their equidistantly sampling counterparts. However, as the required threshold voltage is lowered, the number of samples and, in turn, the data rate and the energy consumed by the overall system increases. In this paper, we present a cubic Hermitian vector-based technique for online compression of asynchronously sampled electrocardiogram signals. The proposed method is computationally efficient data compression. The algorithm has complexity O(n), thus well suited for asynchronous ADCs. Our algorithm requires no data buffering, maintaining the energy advantage of asynchronous ADCs. The proposed method of compression has a compression ratio of up to 90% with achievable percentage root-mean-square difference ratios as a low as 0.97. The algorithm preserves the superior feature-to-feature timing accuracy of asynchronously sampled signals. These advantages are achieved in a computationally efficient manner since algorithm boundary parameters for the signals are extracted a priori.

An optimized lead system for long-term esophageal electrocardiography

Long-term electrocardiography (ECG) featuring adequate atrial and ventricular signal quality is highly desirable. Routinely used surface leads are limited in atrial signal sensitivity and recording capability impeding complete ECG delineation, i.e. in the presence of supraventricular arrhythmias. Long-term esophageal ECG might overcome these limitations but requires a dedicated lead system and recorder design. To this end, we analysed multiple-lead esophageal ECGs with respect to signal quality by describing the ECG waves as a function of the insertion level, interelectrode distance, electrode shape and amplifier's input range. The results derived from clinical data show that two bipolar esophageal leads, an atrial lead with short (15 mm) interelectrode distance and a ventricular lead with long (80 mm) interelectrode distance provide non-inferior ventricular signal strength and superior atrial signal strength compared to standard surface lead II. High atrial signal slope in particular is observed with the atrial esophageal lead. The proposed esophageal lead system in combination with an increased recorder input range of ±20 mV minimizes signal loss due to excessive electrode motion typically observed in esophageal ECGs. The design proposal might help to standardize long-term esophageal ECG registrations and facilitate novel ECG classification systems based on the independent detection of ventricular and atrial electrical activity.