Extracted Attributesa 1. RQ 2. RS 3. RP 4. RL 5. RP in .NET

Integrating Quick Response Code in .NET Extracted Attributesa 1. RQ 2. RS 3. RP 4. RL 5. RP
Table 17.1. Extracted Attributesa 1. RQ 2. RS 3. RP 4. RL 5. RP
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6. RT 7. RS 8. RT 9. P width 10. T width
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11. ST 12. PQ 13. PT 14. LQ 15. ST
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The feature list labels are the normalized distance between the two ducials. For example, RP is the unsigned distance between the end of the P-wave and the R peak.
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17
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The Heartbeat: The Living Biometric
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segments were also removed. The segregation of the task data into 20-s segments allowed for independent block training of the discriminant functions. Eigen Features. The previous approach to ECG analysis relies on ducial attributes that is, features obtained by identifying speci c landmarks from the processed signal. The ducial-based feature extraction was unable to enroll 30% of the collected population (10% due to irregular structure of the ECG trace and 20% due to noise, such as muscle exure). To overcome these two de ciencies, another feature extraction technique is required. The ECG data were aligned using the unsupervised procedure de ned earlier. For eigenanalysis, the entire heartbeat trace is presented to the system. This yields attributes that are always de ned, even for atypical ECG traces discussed below (Figure 17.11). Due to the long feature vectors, eigen attributes are expected to characterize larger populations than ducial-based feature vectors. Since PCA does not generate individual discriminant functions, individuals can be enrolled online without retraining. This approach has proved successful in face recognition, which has exploited eigenspace analysis for human identi cation [30 36]. ECG traces that depart from the idealized shape are, in fact, fairly common in the general population. Anomalies can include multiple extrema, rather than a single peak, and low contrast observable ducials, such as the missing P wave in Figure 17.11. These exceptions, along with sensor noise, imply that ducial processing methods are dif cult to apply to a signi cant segment of the population. We applied principal components analysis (PCA) for feature extraction. The technique, which we call eigenPulse, uses an eigenvector decomposition of the normalized ECG signal. This approach addresses the two weaknesses: 1. We are not limited to a small set of attributes; rather we use an orthonormal basis to represent the most signi cant features for distinguishing the ECG traces. 2. PCA features do not require ducial extraction, which minimizes the exception handling problems and increases enrollment rates.
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Missing P wave.
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17.2 Heartbeat Signals
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Eigen Decomposition of the ECG Trace. The ECG trace is not a random event. It is cyclic with regularly occurring P, R, and T waves (Figure 17.4). If these common cyclic data are removed from an individual s datastream, the remaining information describes the individual s uniqueness or difference to the population norm. The ducial features only capture information about relative position of features within the normalized heartbeat. Because the eigenvectors form an orthonormal basis for the feature space, the expression of normalized heartbeats using this decomposition provides a complete characterization of the ECG. Any normalized heartbeat can be approximated as a linear combination of a subset of the eigenvectors. PCA Attributes and Classifiers. We de ne the data blocks for our experiments in the following manner. The training data characterize the norm of the population. The gallery data represent the enrolled individuals. The probe data represents unlabeled information to be identi ed by the system. The data were block segmented into 20-s intervals in the same manner as the ducial feature extraction analysis. The PCA algorithm consists of four primary steps: construction of the covariance matrix from training data, calculation of the eigenvectors from the covariance matrix, projection of raw probe and gallery data into the eigenspace, and calculation of the distance between projected probe and gallery data streams. Initially, the mean heartbeat xi is computed, where n is number of heartbeats for the ith attribute of an I length heartbeat [Eq. (17.1)]. For heartbeats, an attribute is a normalized time unit. For example, if a heartbeat is normalized to 256 intervals, we have i = 1 to I and I = 256.
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