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The rst step in merging information from 12 different leads is to investigate whether each one of them has discriminative information to offer or not. For this reason, the AC/LDA method is applied on the 12 lead electrocardiogram recordings of every

There are two simple ways to extrapolate the results. On the one hand, one could assume that for a basic geometry the variance or standard deviation of F is constant and apply Equation (14.3). On the other hand, one could assume the coef cient of variation is constant and apply the modi ed form of the equation: = E[F ] 1 E[F ] [F ] (14.10)

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Listing 22-2 encapsulates into a single component all the functionality related to a company. Every time that you need to do something to or get something from a company, you invoke one of the Company component s functions and pass in the CompanyID used to identify the target company; then your functions go out and manipulate that company s data in the database. None of these functions do anything with data associated with the component itself; all these functions directly manipulate the database without any regard to the concept of an instance. In other words, these component functions are the equivalent of class methods in traditional object-oriented terms. To create component functions that are the equivalent of instance methods, an instance of the component needs data or properties directly associated with it, and these instance methods must work with the properties rather than directly with the database. If an instance of a component is created, it contains a scope named This, which is the rough equivalent of the this prefix that we mention in the section Not all telephones are exactly alike, a little earlier in this chapter, in that This refers in this case to This instance. (The difference in the capitalization of This is due to the difference in syntax between ColdFusion and Java, which I used in the Nickel Tour.) So suppose that, inside your component, you set a variable in the This scope, as follows:

subject in the PTB database. The window and subject recognition rates are considerably high for every individual lead. Overall, the subject and window identi cation rates are in the range 85.71% 100% and 82.47% 99.39%. Figure 16.18 shows the subject and window rates of the AC/LDA method for different autocorrelation windows M, when tested on lead V6. The performance for that speci c lead is the greatest achieved with this methodology. Fusion at a decision level can be performed by the rules described earlier. The designer of the system can decide on how conservative the system should be in making decisions either by choosing a rule or by controlling parameter in rules 3 and 4. However, the more conservative the system (i.e., the greater the number of classi er requirements to nalize a decision), the higher the rejection rates R. In the current experimental setup, every subject is tested on a given number of test windows that are offered from the data set. For a subject to be rejected, it is required that all of the corresponding windows are ambiguous. Even though every rule leads to rejection, there is a drop down of 31.42% rejection rate when moving from rule number one to two. This is expected, since it is more likely that half of the total classi ers will avote for a identity rather than all of them. In addition, all subjects are identi ed correctly with both rules. Rules number 3 and 4 result in 100% window and subject identi cation rates. It is, however, expected that for larger data sets the recognition rates will drop for lower values. Rejection is introduced in these cases as well, and especially for high values, the window rejection rate exceeds 30%.

In this code, you create a property for This instance of the Company component. And suppose that you then create a component function named GetCompanyName() that simply returns that property, as follows:

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