AN ANALYSIS OF TWO COMMON REFERENCE POINTS FOR EEGS.

Clinical electroencephalographic (EEG) data varies significantly depending on a number of operational conditions (e.g., the type and placement of electrodes, the type of electrical grounding used). This investigation explores the statistical differences present in two different referential montages: Linked Ear (LE) and Averaged Reference (AR). Each of these accounts for approximately 45% of the data in the TUH EEG Corpus. In this study, we explore the impact this variability has on machine learning performance. We compare the statistical properties of features generated using these two montages, and explore the impact of performance on our standard Hidden Markov Model (HMM) based classification system. We show that a system trained on LE data significantly outperforms one trained only on AR data (77.2% vs. 61.4%). We also demonstrate that performance of a system trained on both data sets is somewhat compromised (71.4% vs. 77.2%). A statistical analysis of the data suggests that mean, variance and channel normalization should be considered. However, cepstral mean subtraction failed to produce an improvement in performance, suggesting that the impact of these statistical differences is subtler.

SEMI-AUTOMATED ANNOTATION OF SIGNAL EVENTS IN CLINICAL EEG DATA.

To be effective, state of the art machine learning technology needs large amounts of annotated data. There are numerous compelling applications in healthcare that can benefit from high performance automated decision support systems provided by deep learning technology, but they lack the comprehensive data resources required to apply sophisticated machine learning models. Further, for economic reasons, it is very difficult to justify the creation of large annotated corpora for these applications. Hence, automated annotation techniques become increasingly important. In this study, we investigated the effectiveness of using an active learning algorithm to automatically annotate a large EEG corpus. The algorithm is designed to annotate six types of EEG events. Two model training schemes, namely threshold-based and volume-based, are evaluated. In the threshold-based scheme the threshold of confidence scores is optimized in the initial training iteration, whereas for the volume-based scheme only a certain amount of data is preserved after each iteration. Recognition performance is improved 2% absolute and the system is capable of automatically annotating previously unlabeled data. Given that the interpretation of clinical EEG data is an exceedingly difficult task, this study provides some evidence that the proposed method is a viable alternative to expensive manual annotation.

Improved EEG Event Classification Using Differential Energy.

Feature extraction for automatic classification of EEG signals typically relies on time frequency representations of the signal. Techniques such as cepstral-based filter banks or wavelets are popular analysis techniques in many signal processing applications including EEG classification. In this paper, we present a comparison of a variety of approaches to estimating and postprocessing features. To further aid in discrimination of periodic signals from aperiodic signals, we add a differential energy term. We evaluate our approaches on the TUH EEG Corpus, which is the largest publicly available EEG corpus and an exceedingly challenging task due to the clinical nature of the data. We demonstrate that a variant of a standard filter bank-based approach, coupled with first and second derivatives, provides a substantial reduction in the overall error rate. The combination of differential energy and derivatives produces a 24% absolute reduction in the error rate and improves our ability to discriminate between signal events and background noise. This relatively simple approach proves to be comparable to other popular feature extraction approaches such as wavelets, but is much more computationally efficient.

Diagnosis of Xanthomonas axonopodis pv. citri, causal agent of citrus canker, in commercial fruits by isolation and PCR-based methods.

AIMS: To show the results of the detection of an EU quarantine organism, Xanthomonas axonopodis pv. citri (Xac), in citrus fruits imported from countries where this bacterium is present, using an integrated approach that includes isolation, pathogenicity assays and molecular techniques. METHODS AND RESULTS: Citrus fruits with canker-like symptoms, exported to Spain from South American countries were analysed by several methods. Bacterial isolation, three conventional polymerase chain reaction (PCR) protocols, and real-time PCR with SYBR Green or a TaqMan probe, were compared. Canker-like lesions were disrupted in PBS buffer, and the extract used for bacterial isolation and DNA extraction followed by PCR amplification. Canker lesions, identified by PCR, showed viable bacteria in eleven of fifteen fruit samples. In 16 out of 130 lesions analysed from these samples, Xac was isolated, and pathogenicity on grapefruit leaves confirmed. By real-time PCR, using SYBR green or a Taqman probe, Xac was detected in 58 and 80 lesions respectively. By conventional PCR the bacterium was detected in 39-52 lesions depending on the protocol employed. CONCLUSIONS: An integrated approach for reliable detection of Xac in lesions of fruit samples, employing several techniques and with real-time PCR using a TaqMan probe as the fastest and most sensitive screening method, has been established and validated and is proposed as a useful tool for the analysis of Xac on fresh fruits. SIGNIFICANCE AND IMPACT OF THE STUDY: This work faces up to the real threat of the importation of citrus fruits that can harbour quarantine bacteria and will be useful in diagnostic laboratories for the analysis of commercial fresh fruits from countries where citrus canker is present.